id	sid	tid	token	lemma	pos
fcis-15765	1	1	frontiers	frontier	NOUN
fcis-15765	1	2	in	in	ADP
fcis-15765	1	3	computing	computing	NOUN
fcis-15765	1	4	and	and	CCONJ
fcis-15765	1	5	intelligent	intelligent	ADJ
fcis-15765	1	6	systems	system	NOUN
fcis-15765	1	7	issn	issn	VERB
fcis-15765	1	8	:	:	PUNCT
fcis-15765	1	9	2832	2832	NUM
fcis-15765	1	10	-	-	SYM
fcis-15765	1	11	6024	6024	NUM
fcis-15765	1	12	|	|	NOUN
fcis-15765	1	13	vol	vol	NOUN
fcis-15765	1	14	.	.	PROPN
fcis-15765	2	1	6	6	NUM
fcis-15765	2	2	,	,	PUNCT
fcis-15765	2	3	no	no	INTJ
fcis-15765	2	4	.	.	NOUN
fcis-15765	2	5	3	3	NUM
fcis-15765	2	6	,	,	PUNCT
fcis-15765	2	7	2023	2023	NUM
fcis-15765	2	8	28	28	NUM
fcis-15765	2	9	a	a	DET
fcis-15765	2	10	survey	survey	NOUN
fcis-15765	2	11	of	of	ADP
fcis-15765	2	12	monocular	monocular	ADJ
fcis-15765	2	13	depth	depth	NOUN
fcis-15765	2	14	estimation	estimation	NOUN
fcis-15765	2	15	based	base	VERB
fcis-15765	2	16	on	on	ADP
fcis-15765	2	17	deep	deep	ADJ
fcis-15765	2	18	learning	learning	NOUN
fcis-15765	2	19	zhen	zhen	PROPN
fcis-15765	2	20	song	song	PROPN
fcis-15765	2	21	,	,	PUNCT
fcis-15765	2	22	jianxing	jianxe	VERB
fcis-15765	2	23	wang	wang	PROPN
fcis-15765	2	24	southwest	southwest	PROPN
fcis-15765	2	25	petroleum	petroleum	PROPN
fcis-15765	2	26	university	university	PROPN
fcis-15765	2	27	,	,	PUNCT
fcis-15765	2	28	chengdu	chengdu	PROPN
fcis-15765	2	29	sichuan	sichuan	PROPN
fcis-15765	2	30	610500	610500	NUM
fcis-15765	3	1	,	,	PUNCT
fcis-15765	3	2	china	china	PROPN
fcis-15765	3	3	abstract	abstract	ADJ
fcis-15765	3	4	:	:	PUNCT
fcis-15765	3	5	depth	depth	NOUN
fcis-15765	3	6	information	information	NOUN
fcis-15765	3	7	is	be	AUX
fcis-15765	3	8	very	very	ADV
fcis-15765	3	9	important	important	ADJ
fcis-15765	3	10	for	for	SCONJ
fcis-15765	3	11	machines	machine	NOUN
fcis-15765	3	12	to	to	PART
fcis-15765	3	13	perceive	perceive	VERB
fcis-15765	3	14	the	the	DET
fcis-15765	3	15	environment	environment	NOUN
fcis-15765	3	16	and	and	CCONJ
fcis-15765	3	17	estimate	estimate	VERB
fcis-15765	3	18	their	their	PRON
fcis-15765	3	19	own	own	ADJ
fcis-15765	3	20	state	state	NOUN
fcis-15765	3	21	.	.	PUNCT
fcis-15765	4	1	significant	significant	ADJ
fcis-15765	4	2	advances	advance	NOUN
fcis-15765	4	3	in	in	ADP
fcis-15765	4	4	robotics	robotic	NOUN
fcis-15765	4	5	engineering	engineering	NOUN
fcis-15765	4	6	and	and	CCONJ
fcis-15765	4	7	self	self	NOUN
fcis-15765	4	8	-	-	PUNCT
fcis-15765	4	9	driving	drive	VERB
fcis-15765	4	10	cars	car	NOUN
fcis-15765	4	11	in	in	ADP
fcis-15765	4	12	recent	recent	ADJ
fcis-15765	4	13	decades	decade	NOUN
fcis-15765	4	14	have	have	AUX
fcis-15765	4	15	increased	increase	VERB
fcis-15765	4	16	the	the	DET
fcis-15765	4	17	demand	demand	NOUN
fcis-15765	4	18	for	for	ADP
fcis-15765	4	19	accurate	accurate	ADJ
fcis-15765	4	20	depth	depth	NOUN
fcis-15765	4	21	measurements	measurement	NOUN
fcis-15765	4	22	.	.	PUNCT
fcis-15765	5	1	traditional	traditional	ADJ
fcis-15765	5	2	depth	depth	NOUN
fcis-15765	5	3	estimation	estimation	NOUN
fcis-15765	5	4	methods	method	NOUN
fcis-15765	5	5	include	include	VERB
fcis-15765	5	6	motion	motion	NOUN
fcis-15765	5	7	structure	structure	NOUN
fcis-15765	5	8	and	and	CCONJ
fcis-15765	5	9	stereo	stereo	NOUN
fcis-15765	5	10	vision	vision	PROPN
fcis-15765	5	11	matching	matching	NOUN
fcis-15765	5	12	,	,	PUNCT
fcis-15765	5	13	but	but	CCONJ
fcis-15765	5	14	these	these	PRON
fcis-15765	5	15	are	be	AUX
fcis-15765	5	16	based	base	VERB
fcis-15765	5	17	on	on	ADP
fcis-15765	5	18	the	the	DET
fcis-15765	5	19	feature	feature	NOUN
fcis-15765	5	20	correspondence	correspondence	NOUN
fcis-15765	5	21	of	of	ADP
fcis-15765	5	22	multiple	multiple	ADJ
fcis-15765	5	23	viewpoints	viewpoint	NOUN
fcis-15765	5	24	,	,	PUNCT
fcis-15765	5	25	and	and	CCONJ
fcis-15765	5	26	at	at	ADP
fcis-15765	5	27	the	the	DET
fcis-15765	5	28	same	same	ADJ
fcis-15765	5	29	time	time	NOUN
fcis-15765	5	30	,	,	PUNCT
fcis-15765	5	31	the	the	DET
fcis-15765	5	32	predicted	predict	VERB
fcis-15765	5	33	depth	depth	NOUN
fcis-15765	5	34	map	map	NOUN
fcis-15765	5	35	is	be	AUX
fcis-15765	5	36	sparse	sparse	ADJ
fcis-15765	5	37	.	.	PUNCT
fcis-15765	6	1	depth	depth	NOUN
fcis-15765	6	2	estimation	estimation	NOUN
fcis-15765	6	3	is	be	AUX
fcis-15765	6	4	a	a	DET
fcis-15765	6	5	traditional	traditional	ADJ
fcis-15765	6	6	task	task	NOUN
fcis-15765	6	7	in	in	ADP
fcis-15765	6	8	computer	computer	NOUN
fcis-15765	6	9	vision	vision	NOUN
fcis-15765	6	10	that	that	PRON
fcis-15765	6	11	can	can	AUX
fcis-15765	6	12	be	be	AUX
fcis-15765	6	13	properly	properly	ADV
fcis-15765	6	14	predicted	predict	VERB
fcis-15765	6	15	by	by	ADP
fcis-15765	6	16	applying	apply	VERB
fcis-15765	6	17	a	a	DET
fcis-15765	6	18	variety	variety	NOUN
fcis-15765	6	19	of	of	ADP
fcis-15765	6	20	procedures	procedure	NOUN
fcis-15765	6	21	,	,	PUNCT
fcis-15765	6	22	whereas	whereas	SCONJ
fcis-15765	6	23	inferring	infer	VERB
fcis-15765	6	24	depth	depth	NOUN
fcis-15765	6	25	information	information	NOUN
fcis-15765	6	26	from	from	ADP
fcis-15765	6	27	a	a	DET
fcis-15765	6	28	single	single	ADJ
fcis-15765	6	29	image	image	NOUN
fcis-15765	6	30	is	be	AUX
fcis-15765	6	31	an	an	DET
fcis-15765	6	32	ill	ill	ADV
fcis-15765	6	33	-	-	PUNCT
fcis-15765	6	34	posed	pose	VERB
fcis-15765	6	35	problem	problem	NOUN
fcis-15765	6	36	.	.	PUNCT
fcis-15765	7	1	the	the	DET
fcis-15765	7	2	main	main	ADJ
fcis-15765	7	3	objective	objective	NOUN
fcis-15765	7	4	of	of	ADP
fcis-15765	7	5	this	this	DET
fcis-15765	7	6	paper	paper	NOUN
fcis-15765	7	7	is	be	AUX
fcis-15765	7	8	to	to	PART
fcis-15765	7	9	provide	provide	VERB
fcis-15765	7	10	a	a	DET
fcis-15765	7	11	brief	brief	ADJ
fcis-15765	7	12	overview	overview	NOUN
fcis-15765	7	13	of	of	ADP
fcis-15765	7	14	the	the	DET
fcis-15765	7	15	development	development	NOUN
fcis-15765	7	16	of	of	ADP
fcis-15765	7	17	monocular	monocular	ADJ
fcis-15765	7	18	depth	depth	NOUN
fcis-15765	7	19	estimation	estimation	NOUN
fcis-15765	7	20	techniques	technique	NOUN
fcis-15765	7	21	based	base	VERB
fcis-15765	7	22	on	on	ADP
fcis-15765	7	23	deep	deep	ADJ
fcis-15765	7	24	learning	learning	NOUN
fcis-15765	7	25	.	.	PUNCT
fcis-15765	8	1	this	this	DET
fcis-15765	8	2	article	article	NOUN
fcis-15765	8	3	attempts	attempt	VERB
fcis-15765	8	4	to	to	PART
fcis-15765	8	5	give	give	VERB
fcis-15765	8	6	an	an	DET
fcis-15765	8	7	overview	overview	NOUN
fcis-15765	8	8	of	of	ADP
fcis-15765	8	9	supervised	supervised	ADJ
fcis-15765	8	10	,	,	PUNCT
fcis-15765	8	11	unsupervised	unsupervised	ADJ
fcis-15765	8	12	,	,	PUNCT
fcis-15765	8	13	and	and	CCONJ
fcis-15765	8	14	datasets	dataset	NOUN
fcis-15765	8	15	and	and	CCONJ
fcis-15765	8	16	evaluation	evaluation	NOUN
fcis-15765	8	17	metrics	metric	NOUN
fcis-15765	8	18	.	.	PUNCT
fcis-15765	9	1	we	we	PRON
fcis-15765	9	2	conclude	conclude	VERB
fcis-15765	9	3	with	with	ADP
fcis-15765	9	4	a	a	DET
fcis-15765	9	5	brief	brief	ADJ
fcis-15765	9	6	analysis	analysis	NOUN
fcis-15765	9	7	of	of	ADP
fcis-15765	9	8	future	future	ADJ
fcis-15765	9	9	developments	development	NOUN
fcis-15765	9	10	.	.	PUNCT
fcis-15765	10	1	keywords	keyword	NOUN
fcis-15765	10	2	:	:	PUNCT
fcis-15765	10	3	depth	depth	NOUN
fcis-15765	10	4	estimation	estimation	NOUN
fcis-15765	10	5	;	;	PUNCT
fcis-15765	10	6	monocular	monocular	ADJ
fcis-15765	10	7	depth	depth	NOUN
fcis-15765	10	8	estimation	estimation	NOUN
fcis-15765	10	9	;	;	PUNCT
fcis-15765	10	10	supervised	supervised	ADJ
fcis-15765	10	11	;	;	PUNCT
fcis-15765	10	12	unsupervised	unsupervised	ADJ
fcis-15765	10	13	.	.	PUNCT
fcis-15765	11	1	1	1	X
fcis-15765	11	2	.	.	X
fcis-15765	11	3	introduction	introduction	NOUN
fcis-15765	11	4	depth	depth	NOUN
fcis-15765	11	5	estimation	estimation	NOUN
fcis-15765	11	6	is	be	AUX
fcis-15765	11	7	crucial	crucial	ADJ
fcis-15765	11	8	in	in	ADP
fcis-15765	11	9	computer	computer	NOUN
fcis-15765	11	10	vision	vision	NOUN
fcis-15765	11	11	,	,	PUNCT
fcis-15765	11	12	especially	especially	ADV
fcis-15765	11	13	for	for	ADP
fcis-15765	11	14	understanding	understand	VERB
fcis-15765	11	15	geometric	geometric	ADJ
fcis-15765	11	16	relationships	relationship	NOUN
fcis-15765	11	17	in	in	ADP
fcis-15765	11	18	scenes	scene	NOUN
fcis-15765	11	19	.	.	PUNCT
fcis-15765	12	1	the	the	DET
fcis-15765	12	2	task	task	NOUN
fcis-15765	12	3	consists	consist	VERB
fcis-15765	12	4	of	of	ADP
fcis-15765	12	5	predicting	predict	VERB
fcis-15765	12	6	the	the	DET
fcis-15765	12	7	distance	distance	NOUN
fcis-15765	12	8	between	between	ADP
fcis-15765	12	9	the	the	DET
fcis-15765	12	10	projection	projection	NOUN
fcis-15765	12	11	center	center	NOUN
fcis-15765	12	12	and	and	CCONJ
fcis-15765	12	13	the	the	DET
fcis-15765	12	14	3d	3d	PROPN
fcis-15765	12	15	point	point	NOUN
fcis-15765	12	16	corresponding	correspond	VERB
fcis-15765	12	17	to	to	ADP
fcis-15765	12	18	each	each	DET
fcis-15765	12	19	pixel	pixel	NOUN
fcis-15765	12	20	.	.	PUNCT
fcis-15765	13	1	depth	depth	NOUN
fcis-15765	13	2	estimation	estimation	NOUN
fcis-15765	13	3	has	have	VERB
fcis-15765	13	4	direct	direct	ADJ
fcis-15765	13	5	relevance	relevance	NOUN
fcis-15765	13	6	in	in	ADP
fcis-15765	13	7	downstream	downstream	ADJ
fcis-15765	13	8	applications	application	NOUN
fcis-15765	13	9	such	such	ADJ
fcis-15765	13	10	as	as	ADP
fcis-15765	13	11	3d	3d	PROPN
fcis-15765	13	12	modeling	modeling	NOUN
fcis-15765	13	13	,	,	PUNCT
fcis-15765	13	14	robotics	robotic	NOUN
fcis-15765	13	15	,	,	PUNCT
fcis-15765	13	16	and	and	CCONJ
fcis-15765	13	17	self	self	NOUN
fcis-15765	13	18	-	-	PUNCT
fcis-15765	13	19	driving	drive	VERB
fcis-15765	13	20	cars	car	NOUN
fcis-15765	13	21	.	.	PUNCT
fcis-15765	14	1	several	several	ADJ
fcis-15765	14	2	studies	study	NOUN
fcis-15765	14	3	[	[	X
fcis-15765	14	4	1	1	X
fcis-15765	14	5	]	]	PUNCT
fcis-15765	14	6	have	have	AUX
fcis-15765	14	7	shown	show	VERB
fcis-15765	14	8	that	that	DET
fcis-15765	14	9	depth	depth	NOUN
fcis-15765	14	10	estimation	estimation	NOUN
fcis-15765	14	11	is	be	AUX
fcis-15765	14	12	crucial	crucial	ADJ
fcis-15765	14	13	for	for	ADP
fcis-15765	14	14	action	action	NOUN
fcis-15765	14	15	reasoning	reasoning	NOUN
fcis-15765	14	16	and	and	CCONJ
fcis-15765	14	17	execution	execution	NOUN
fcis-15765	14	18	.	.	PUNCT
fcis-15765	15	1	currently	currently	ADV
fcis-15765	15	2	,	,	PUNCT
fcis-15765	15	3	lidar	lidar	NOUN
fcis-15765	15	4	,	,	PUNCT
fcis-15765	15	5	structured	structure	VERB
fcis-15765	15	6	light	light	ADJ
fcis-15765	15	7	depth	depth	NOUN
fcis-15765	15	8	sensors	sensor	NOUN
fcis-15765	15	9	and	and	CCONJ
fcis-15765	15	10	time	time	NOUN
fcis-15765	15	11	-	-	PUNCT
fcis-15765	15	12	of	of	ADP
fcis-15765	15	13	-	-	PUNCT
fcis-15765	15	14	flight	flight	NOUN
fcis-15765	15	15	sensors	sensor	NOUN
fcis-15765	15	16	are	be	AUX
fcis-15765	15	17	used	use	VERB
fcis-15765	15	18	to	to	PART
fcis-15765	15	19	obtain	obtain	VERB
fcis-15765	15	20	depth	depth	NOUN
fcis-15765	15	21	information	information	NOUN
fcis-15765	16	1	[	[	X
fcis-15765	16	2	2][3	2][3	X
fcis-15765	16	3	]	]	X
fcis-15765	16	4	.	.	PUNCT
fcis-15765	17	1	these	these	DET
fcis-15765	17	2	active	active	ADJ
fcis-15765	17	3	depth	depth	NOUN
fcis-15765	17	4	sensors	sensor	NOUN
fcis-15765	17	5	tend	tend	VERB
fcis-15765	17	6	to	to	PART
fcis-15765	17	7	be	be	AUX
fcis-15765	17	8	heavy	heavy	ADJ
fcis-15765	17	9	,	,	PUNCT
fcis-15765	17	10	expensive	expensive	ADJ
fcis-15765	17	11	,	,	PUNCT
fcis-15765	17	12	and	and	CCONJ
fcis-15765	17	13	power	power	NOUN
fcis-15765	17	14	-	-	PUNCT
fcis-15765	17	15	hungry	hungry	ADJ
fcis-15765	17	16	.	.	PUNCT
fcis-15765	18	1	at	at	ADP
fcis-15765	18	2	the	the	DET
fcis-15765	18	3	same	same	ADJ
fcis-15765	18	4	time	time	NOUN
fcis-15765	18	5	,	,	PUNCT
fcis-15765	18	6	they	they	PRON
fcis-15765	18	7	suffer	suffer	VERB
fcis-15765	18	8	from	from	ADP
fcis-15765	18	9	noise	noise	NOUN
fcis-15765	18	10	and	and	CCONJ
fcis-15765	18	11	artifacts	artifact	NOUN
fcis-15765	18	12	,	,	PUNCT
fcis-15765	18	13	especially	especially	ADV
fcis-15765	18	14	from	from	ADP
fcis-15765	18	15	reflective	reflective	ADJ
fcis-15765	18	16	or	or	CCONJ
fcis-15765	18	17	transparent	transparent	ADJ
fcis-15765	18	18	surfaces	surface	NOUN
fcis-15765	18	19	.	.	PUNCT
fcis-15765	19	1	in	in	ADP
fcis-15765	19	2	addition	addition	NOUN
fcis-15765	19	3	,	,	PUNCT
fcis-15765	19	4	depth	depth	NOUN
fcis-15765	19	5	information	information	NOUN
fcis-15765	19	6	can	can	AUX
fcis-15765	19	7	also	also	ADV
fcis-15765	19	8	be	be	AUX
fcis-15765	19	9	obtained	obtain	VERB
fcis-15765	19	10	from	from	ADP
fcis-15765	19	11	depth	depth	NOUN
fcis-15765	19	12	defocus	defocus	NOUN
fcis-15765	19	13	[	[	X
fcis-15765	19	14	4][5	4][5	NOUN
fcis-15765	19	15	]	]	X
fcis-15765	19	16	,	,	PUNCT
fcis-15765	19	17	multi	multi	ADJ
fcis-15765	19	18	-	-	ADJ
fcis-15765	19	19	view	view	ADJ
fcis-15765	19	20	stereo	stereo	NOUN
fcis-15765	19	21	(	(	PUNCT
fcis-15765	19	22	mvs	mvs	PROPN
fcis-15765	19	23	)	)	PUNCT
fcis-15765	20	1	[	[	X
fcis-15765	20	2	6][7	6][7	NUM
fcis-15765	20	3	]	]	PUNCT
fcis-15765	20	4	,	,	PUNCT
fcis-15765	20	5	and	and	CCONJ
fcis-15765	20	6	obtained	obtain	VERB
fcis-15765	20	7	structure	structure	NOUN
fcis-15765	20	8	from	from	ADP
fcis-15765	20	9	motion	motion	NOUN
fcis-15765	20	10	(	(	PUNCT
fcis-15765	20	11	sfm	sfm	NOUN
fcis-15765	20	12	)	)	PUNCT
fcis-15765	21	1	[	[	X
fcis-15765	21	2	8	8	NUM
fcis-15765	21	3	]	]	PUNCT
fcis-15765	21	4	.	.	PUNCT
fcis-15765	22	1	however	however	ADV
fcis-15765	22	2	,	,	PUNCT
fcis-15765	22	3	these	these	DET
fcis-15765	22	4	methods	method	NOUN
fcis-15765	22	5	are	be	AUX
fcis-15765	22	6	either	either	CCONJ
fcis-15765	22	7	timeconsuming	timeconsuming	ADJ
fcis-15765	22	8	or	or	CCONJ
fcis-15765	22	9	have	have	VERB
fcis-15765	22	10	low	low	ADJ
fcis-15765	22	11	depth	depth	NOUN
fcis-15765	22	12	accuracy	accuracy	NOUN
fcis-15765	22	13	.	.	PUNCT
fcis-15765	23	1	therefore	therefore	ADV
fcis-15765	23	2	,	,	PUNCT
fcis-15765	23	3	depth	depth	NOUN
fcis-15765	23	4	estimation	estimation	NOUN
fcis-15765	23	5	using	use	VERB
fcis-15765	23	6	a	a	DET
fcis-15765	23	7	single	single	ADJ
fcis-15765	23	8	image	image	NOUN
fcis-15765	23	9	from	from	ADP
fcis-15765	23	10	an	an	DET
fcis-15765	23	11	rgb	rgb	PROPN
fcis-15765	23	12	camera	camera	NOUN
fcis-15765	23	13	is	be	AUX
fcis-15765	23	14	an	an	DET
fcis-15765	23	15	attractive	attractive	ADJ
fcis-15765	23	16	alternative	alternative	NOUN
fcis-15765	23	17	to	to	ADP
fcis-15765	23	18	the	the	DET
fcis-15765	23	19	depth	depth	NOUN
fcis-15765	23	20	estimation	estimation	NOUN
fcis-15765	23	21	methods	method	NOUN
fcis-15765	23	22	described	describe	VERB
fcis-15765	23	23	above	above	ADV
fcis-15765	23	24	because	because	SCONJ
fcis-15765	23	25	of	of	ADP
fcis-15765	23	26	its	its	PRON
fcis-15765	23	27	compactness	compactness	NOUN
fcis-15765	23	28	,	,	PUNCT
fcis-15765	23	29	cheapness	cheapness	NOUN
fcis-15765	23	30	,	,	PUNCT
fcis-15765	23	31	and	and	CCONJ
fcis-15765	23	32	low	low	ADJ
fcis-15765	23	33	power	power	NOUN
fcis-15765	23	34	consumption	consumption	NOUN
fcis-15765	23	35	.	.	PUNCT
fcis-15765	24	1	in	in	ADP
fcis-15765	24	2	the	the	DET
fcis-15765	24	3	past	past	ADJ
fcis-15765	24	4	decade	decade	NOUN
fcis-15765	24	5	,	,	PUNCT
fcis-15765	24	6	inspired	inspire	VERB
fcis-15765	24	7	by	by	ADP
fcis-15765	24	8	the	the	DET
fcis-15765	24	9	success	success	NOUN
fcis-15765	24	10	of	of	ADP
fcis-15765	24	11	deep	deep	ADJ
fcis-15765	24	12	learning	learning	NOUN
fcis-15765	24	13	on	on	ADP
fcis-15765	24	14	high	high	ADJ
fcis-15765	24	15	-	-	PUNCT
fcis-15765	24	16	level	level	NOUN
fcis-15765	24	17	vision	vision	NOUN
fcis-15765	24	18	tasks	task	NOUN
fcis-15765	24	19	[	[	X
fcis-15765	24	20	9][10	9][10	NUM
fcis-15765	24	21	]	]	PUNCT
fcis-15765	24	22	,	,	PUNCT
fcis-15765	24	23	monocular	monocular	ADJ
fcis-15765	24	24	depth	depth	NOUN
fcis-15765	24	25	estimation	estimation	NOUN
fcis-15765	24	26	based	base	VERB
fcis-15765	24	27	on	on	ADP
fcis-15765	24	28	supervised	supervised	ADJ
fcis-15765	24	29	learning	learning	NOUN
fcis-15765	24	30	has	have	AUX
fcis-15765	24	31	been	be	AUX
fcis-15765	24	32	extensively	extensively	ADV
fcis-15765	24	33	studied	study	VERB
fcis-15765	24	34	.	.	PUNCT
fcis-15765	25	1	it	it	PRON
fcis-15765	25	2	transforms	transform	VERB
fcis-15765	25	3	the	the	DET
fcis-15765	25	4	monocular	monocular	ADJ
fcis-15765	25	5	depth	depth	NOUN
fcis-15765	25	6	estimation	estimation	NOUN
fcis-15765	25	7	problem	problem	NOUN
fcis-15765	25	8	into	into	ADP
fcis-15765	25	9	a	a	DET
fcis-15765	25	10	pixel	pixel	ADJ
fcis-15765	25	11	-	-	ADJ
fcis-15765	25	12	wise	wise	ADJ
fcis-15765	25	13	regression	regression	NOUN
fcis-15765	25	14	problem	problem	NOUN
fcis-15765	25	15	and	and	CCONJ
fcis-15765	25	16	achieves	achieve	VERB
fcis-15765	25	17	impressive	impressive	ADJ
fcis-15765	25	18	performance	performance	NOUN
fcis-15765	26	1	[	[	X
fcis-15765	26	2	11][12	11][12	PROPN
fcis-15765	26	3	]	]	X
fcis-15765	26	4	.	.	PUNCT
fcis-15765	27	1	however	however	ADV
fcis-15765	27	2	,	,	PUNCT
fcis-15765	27	3	supervised	supervised	ADJ
fcis-15765	27	4	learning	learning	NOUN
fcis-15765	27	5	methods	method	NOUN
fcis-15765	27	6	rely	rely	VERB
fcis-15765	27	7	on	on	ADP
fcis-15765	27	8	large	large	ADJ
fcis-15765	27	9	labeled	label	VERB
fcis-15765	27	10	rgb	rgb	PROPN
fcis-15765	27	11	-	-	PUNCT
fcis-15765	27	12	d	d	PROPN
fcis-15765	27	13	datasets	dataset	NOUN
fcis-15765	27	14	,	,	PUNCT
fcis-15765	27	15	which	which	PRON
fcis-15765	27	16	are	be	AUX
fcis-15765	27	17	expensive	expensive	ADJ
fcis-15765	27	18	and	and	CCONJ
fcis-15765	27	19	overburdened	overburden	VERB
fcis-15765	27	20	.	.	PUNCT
fcis-15765	28	1	to	to	PART
fcis-15765	28	2	avoid	avoid	VERB
fcis-15765	28	3	the	the	DET
fcis-15765	28	4	need	need	NOUN
fcis-15765	28	5	for	for	ADP
fcis-15765	28	6	large	large	ADJ
fcis-15765	28	7	-	-	PUNCT
fcis-15765	28	8	scale	scale	NOUN
fcis-15765	28	9	labeled	label	VERB
fcis-15765	28	10	datasets	dataset	NOUN
fcis-15765	28	11	,	,	PUNCT
fcis-15765	28	12	unsupervised	unsupervised	ADJ
fcis-15765	28	13	monocular	monocular	ADJ
fcis-15765	28	14	depth	depth	NOUN
fcis-15765	28	15	estimation	estimation	NOUN
fcis-15765	28	16	methods	method	NOUN
fcis-15765	28	17	have	have	AUX
fcis-15765	28	18	recently	recently	ADV
fcis-15765	28	19	emerged	emerge	VERB
fcis-15765	28	20	in	in	ADP
fcis-15765	28	21	the	the	DET
fcis-15765	28	22	literature	literature	NOUN
fcis-15765	28	23	.	.	PUNCT
fcis-15765	29	1	these	these	DET
fcis-15765	29	2	methods	method	NOUN
fcis-15765	29	3	mimic	mimic	VERB
fcis-15765	29	4	human	human	ADJ
fcis-15765	29	5	binocular	binocular	ADJ
fcis-15765	29	6	or	or	CCONJ
fcis-15765	29	7	monocular	monocular	ADJ
fcis-15765	29	8	vision	vision	NOUN
fcis-15765	29	9	capabilities	capability	NOUN
fcis-15765	29	10	.	.	PUNCT
fcis-15765	30	1	among	among	ADP
fcis-15765	30	2	them	they	PRON
fcis-15765	30	3	,	,	PUNCT
fcis-15765	30	4	the	the	DET
fcis-15765	30	5	ground	ground	NOUN
fcis-15765	30	6	-	-	PUNCT
fcis-15765	30	7	truth	truth	NOUN
fcis-15765	30	8	-	-	PUNCT
fcis-15765	30	9	based	base	VERB
fcis-15765	30	10	loss	loss	NOUN
fcis-15765	30	11	is	be	AUX
fcis-15765	30	12	replaced	replace	VERB
fcis-15765	30	13	by	by	ADP
fcis-15765	30	14	the	the	DET
fcis-15765	30	15	image	image	NOUN
fcis-15765	30	16	reconstruction	reconstruction	NOUN
fcis-15765	30	17	loss	loss	NOUN
fcis-15765	30	18	[	[	X
fcis-15765	30	19	13][14	13][14	PROPN
fcis-15765	30	20	]	]	PUNCT
fcis-15765	30	21	.	.	PUNCT
fcis-15765	31	1	with	with	ADP
fcis-15765	31	2	the	the	DET
fcis-15765	31	3	rapid	rapid	ADJ
fcis-15765	31	4	development	development	NOUN
fcis-15765	31	5	of	of	ADP
fcis-15765	31	6	deep	deep	ADJ
fcis-15765	31	7	neural	neural	ADJ
fcis-15765	31	8	networks	network	NOUN
fcis-15765	31	9	,	,	PUNCT
fcis-15765	31	10	deep	deep	ADJ
fcis-15765	31	11	learning	learning	NOUN
fcis-15765	31	12	-	-	PUNCT
fcis-15765	31	13	based	base	VERB
fcis-15765	31	14	monocular	monocular	ADJ
fcis-15765	31	15	depth	depth	NOUN
fcis-15765	31	16	estimation	estimation	NOUN
fcis-15765	31	17	has	have	AUX
fcis-15765	31	18	been	be	AUX
fcis-15765	31	19	extensively	extensively	ADV
fcis-15765	31	20	studied	study	VERB
fcis-15765	31	21	and	and	CCONJ
fcis-15765	31	22	achieved	achieve	VERB
fcis-15765	31	23	good	good	ADJ
fcis-15765	31	24	results	result	NOUN
fcis-15765	31	25	in	in	ADP
fcis-15765	31	26	accuracy	accuracy	NOUN
fcis-15765	31	27	.	.	PUNCT
fcis-15765	32	1	meanwhile	meanwhile	ADV
fcis-15765	32	2	,	,	PUNCT
fcis-15765	32	3	a	a	DET
fcis-15765	32	4	dense	dense	ADJ
fcis-15765	32	5	depth	depth	NOUN
fcis-15765	32	6	map	map	NOUN
fcis-15765	32	7	is	be	AUX
fcis-15765	32	8	estimated	estimate	VERB
fcis-15765	32	9	from	from	ADP
fcis-15765	32	10	a	a	DET
fcis-15765	32	11	single	single	ADJ
fcis-15765	32	12	image	image	NOUN
fcis-15765	32	13	by	by	ADP
fcis-15765	32	14	a	a	DET
fcis-15765	32	15	deep	deep	ADJ
fcis-15765	32	16	neural	neural	ADJ
fcis-15765	32	17	network	network	NOUN
fcis-15765	32	18	in	in	ADP
fcis-15765	32	19	an	an	DET
fcis-15765	32	20	end	end	NOUN
fcis-15765	32	21	-	-	PUNCT
fcis-15765	32	22	to	to	ADP
fcis-15765	32	23	-	-	PUNCT
fcis-15765	32	24	end	end	NOUN
fcis-15765	32	25	manner	manner	NOUN
fcis-15765	32	26	.	.	PUNCT
fcis-15765	33	1	in	in	ADP
fcis-15765	33	2	order	order	NOUN
fcis-15765	33	3	to	to	PART
fcis-15765	33	4	improve	improve	VERB
fcis-15765	33	5	the	the	DET
fcis-15765	33	6	accuracy	accuracy	NOUN
fcis-15765	33	7	of	of	ADP
fcis-15765	33	8	depth	depth	NOUN
fcis-15765	33	9	estimation	estimation	NOUN
fcis-15765	33	10	,	,	PUNCT
fcis-15765	33	11	different	different	ADJ
fcis-15765	33	12	types	type	NOUN
fcis-15765	33	13	of	of	ADP
fcis-15765	33	14	network	network	NOUN
fcis-15765	33	15	frameworks	framework	NOUN
fcis-15765	33	16	,	,	PUNCT
fcis-15765	33	17	loss	loss	NOUN
fcis-15765	33	18	functions	function	NOUN
fcis-15765	33	19	and	and	CCONJ
fcis-15765	33	20	training	training	NOUN
fcis-15765	33	21	strategies	strategy	NOUN
fcis-15765	33	22	have	have	AUX
fcis-15765	33	23	been	be	AUX
fcis-15765	33	24	proposed	propose	VERB
fcis-15765	33	25	successively	successively	ADV
fcis-15765	33	26	.	.	PUNCT
fcis-15765	34	1	therefore	therefore	ADV
fcis-15765	34	2	,	,	PUNCT
fcis-15765	34	3	this	this	DET
fcis-15765	34	4	paper	paper	NOUN
fcis-15765	34	5	provides	provide	VERB
fcis-15765	34	6	an	an	DET
fcis-15765	34	7	overview	overview	NOUN
fcis-15765	34	8	of	of	ADP
fcis-15765	34	9	current	current	ADJ
fcis-15765	34	10	deep	deep	ADJ
fcis-15765	34	11	learning	learning	NOUN
fcis-15765	34	12	-	-	PUNCT
fcis-15765	34	13	based	base	VERB
fcis-15765	34	14	methods	method	NOUN
fcis-15765	34	15	for	for	ADP
fcis-15765	34	16	monocular	monocular	ADJ
fcis-15765	34	17	depth	depth	NOUN
fcis-15765	34	18	estimation	estimation	NOUN
fcis-15765	34	19	.	.	PUNCT
fcis-15765	35	1	first	first	ADV
fcis-15765	35	2	,	,	PUNCT
fcis-15765	35	3	we	we	PRON
fcis-15765	35	4	summarize	summarize	VERB
fcis-15765	35	5	several	several	ADJ
fcis-15765	35	6	widely	widely	ADV
fcis-15765	35	7	used	use	VERB
fcis-15765	35	8	datasets	dataset	NOUN
fcis-15765	35	9	and	and	CCONJ
fcis-15765	35	10	evaluation	evaluation	NOUN
fcis-15765	35	11	metrics	metric	NOUN
fcis-15765	35	12	in	in	ADP
fcis-15765	35	13	deep	deep	ADJ
fcis-15765	35	14	learning	learning	NOUN
fcis-15765	35	15	-	-	PUNCT
fcis-15765	35	16	based	base	VERB
fcis-15765	35	17	depth	depth	NOUN
fcis-15765	35	18	estimation	estimation	NOUN
fcis-15765	35	19	.	.	PUNCT
fcis-15765	36	1	furthermore	furthermore	ADV
fcis-15765	36	2	,	,	PUNCT
fcis-15765	36	3	we	we	PRON
fcis-15765	36	4	review	review	VERB
fcis-15765	36	5	some	some	DET
fcis-15765	36	6	representative	representative	ADJ
fcis-15765	36	7	methods	method	NOUN
fcis-15765	36	8	according	accord	VERB
fcis-15765	36	9	to	to	ADP
fcis-15765	36	10	different	different	ADJ
fcis-15765	36	11	training	training	NOUN
fcis-15765	36	12	methods	method	NOUN
fcis-15765	36	13	:	:	PUNCT
fcis-15765	36	14	supervised	supervised	ADJ
fcis-15765	36	15	and	and	CCONJ
fcis-15765	36	16	unsupervised	unsupervised	ADJ
fcis-15765	36	17	.	.	PUNCT
fcis-15765	37	1	finally	finally	ADV
fcis-15765	37	2	,	,	PUNCT
fcis-15765	37	3	we	we	PRON
fcis-15765	37	4	discuss	discuss	VERB
fcis-15765	37	5	the	the	DET
fcis-15765	37	6	challenges	challenge	NOUN
fcis-15765	37	7	in	in	ADP
fcis-15765	37	8	monocular	monocular	ADJ
fcis-15765	37	9	depth	depth	NOUN
fcis-15765	37	10	estimation	estimation	NOUN
fcis-15765	37	11	and	and	CCONJ
fcis-15765	37	12	provide	provide	VERB
fcis-15765	37	13	some	some	DET
fcis-15765	37	14	ideas	idea	NOUN
fcis-15765	37	15	for	for	ADP
fcis-15765	37	16	future	future	ADJ
fcis-15765	37	17	research	research	NOUN
fcis-15765	37	18	.	.	PUNCT
fcis-15765	38	1	2	2	X
fcis-15765	38	2	.	.	X
fcis-15765	38	3	traditional	traditional	ADJ
fcis-15765	38	4	depth	depth	NOUN
fcis-15765	38	5	estimation	estimation	NOUN
fcis-15765	38	6	methods	method	NOUN
fcis-15765	38	7	traditional	traditional	ADJ
fcis-15765	38	8	depth	depth	NOUN
fcis-15765	38	9	estimation	estimation	NOUN
fcis-15765	38	10	methods	method	NOUN
fcis-15765	38	11	mostly	mostly	ADV
fcis-15765	38	12	rely	rely	VERB
fcis-15765	38	13	on	on	ADP
fcis-15765	38	14	the	the	DET
fcis-15765	38	15	assumption	assumption	NOUN
fcis-15765	38	16	of	of	ADP
fcis-15765	38	17	observations	observation	NOUN
fcis-15765	38	18	of	of	ADP
fcis-15765	38	19	the	the	DET
fcis-15765	38	20	scene	scene	NOUN
fcis-15765	38	21	,	,	PUNCT
fcis-15765	38	22	exploiting	exploit	VERB
fcis-15765	38	23	visual	visual	ADJ
fcis-15765	38	24	cues	cue	NOUN
fcis-15765	38	25	to	to	PART
fcis-15765	38	26	estimate	estimate	VERB
fcis-15765	38	27	the	the	DET
fcis-15765	38	28	depth	depth	NOUN
fcis-15765	38	29	of	of	ADP
fcis-15765	38	30	a	a	DET
fcis-15765	38	31	given	give	VERB
fcis-15765	38	32	scene	scene	NOUN
fcis-15765	38	33	.	.	PUNCT
fcis-15765	39	1	traditional	traditional	ADJ
fcis-15765	39	2	methods	method	NOUN
fcis-15765	39	3	can	can	AUX
fcis-15765	39	4	be	be	AUX
fcis-15765	39	5	divided	divide	VERB
fcis-15765	39	6	into	into	ADP
fcis-15765	39	7	two	two	NUM
fcis-15765	39	8	categories	category	NOUN
fcis-15765	39	9	:	:	PUNCT
fcis-15765	39	10	geometric	geometric	ADJ
fcis-15765	39	11	calculation	calculation	NOUN
fcis-15765	39	12	methods	method	NOUN
fcis-15765	39	13	and	and	CCONJ
fcis-15765	39	14	sensor	sensor	NOUN
fcis-15765	39	15	detection	detection	NOUN
fcis-15765	39	16	methods	method	NOUN
fcis-15765	39	17	.	.	PUNCT
fcis-15765	40	1	geometric	geometric	ADJ
fcis-15765	40	2	algorithms	algorithm	NOUN
fcis-15765	40	3	recover	recover	VERB
fcis-15765	40	4	3d	3d	NUM
fcis-15765	40	5	structures	structure	NOUN
fcis-15765	40	6	from	from	ADP
fcis-15765	40	7	two	two	NUM
fcis-15765	40	8	images	image	NOUN
fcis-15765	40	9	based	base	VERB
fcis-15765	40	10	on	on	ADP
fcis-15765	40	11	geometric	geometric	ADJ
fcis-15765	40	12	constraints	constraint	NOUN
fcis-15765	40	13	.	.	PUNCT
fcis-15765	41	1	for	for	ADP
fcis-15765	41	2	example	example	NOUN
fcis-15765	41	3	,	,	PUNCT
fcis-15765	41	4	structure	structure	NOUN
fcis-15765	41	5	from	from	ADP
fcis-15765	41	6	motion	motion	NOUN
fcis-15765	41	7	(	(	PUNCT
fcis-15765	41	8	sfm	sfm	NOUN
fcis-15765	41	9	)	)	PUNCT
fcis-15765	42	1	[	[	X
fcis-15765	42	2	15	15	NUM
fcis-15765	42	3	]	]	X
fcis-15765	42	4	is	be	AUX
fcis-15765	42	5	a	a	DET
fcis-15765	42	6	representative	representative	ADJ
fcis-15765	42	7	method	method	NOUN
fcis-15765	42	8	for	for	ADP
fcis-15765	42	9	estimating	estimate	VERB
fcis-15765	42	10	3d	3d	NUM
fcis-15765	42	11	structures	structure	NOUN
fcis-15765	42	12	from	from	ADP
fcis-15765	42	13	a	a	DET
fcis-15765	42	14	series	series	NOUN
fcis-15765	42	15	of	of	ADP
fcis-15765	42	16	2d	2d	NUM
fcis-15765	42	17	image	image	NOUN
fcis-15765	42	18	sequences	sequence	NOUN
fcis-15765	42	19	,	,	PUNCT
fcis-15765	42	20	and	and	CCONJ
fcis-15765	42	21	has	have	AUX
fcis-15765	42	22	been	be	AUX
fcis-15765	42	23	successfully	successfully	ADV
fcis-15765	42	24	applied	apply	VERB
fcis-15765	42	25	in	in	ADP
fcis-15765	42	26	3d	3d	NUM
fcis-15765	42	27	reconstruction	reconstruction	NOUN
fcis-15765	42	28	[	[	X
fcis-15765	42	29	16	16	NUM
fcis-15765	42	30	]	]	PUNCT
fcis-15765	42	31	and	and	CCONJ
fcis-15765	42	32	slam	slam	VERB
fcis-15765	42	33	[	[	X
fcis-15765	42	34	17	17	NUM
fcis-15765	42	35	]	]	PUNCT
fcis-15765	42	36	.	.	PUNCT
fcis-15765	43	1	sfm	sfm	PROPN
fcis-15765	43	2	can	can	AUX
fcis-15765	43	3	handle	handle	VERB
fcis-15765	43	4	the	the	DET
fcis-15765	43	5	depth	depth	NOUN
fcis-15765	43	6	of	of	ADP
fcis-15765	43	7	sparse	sparse	ADJ
fcis-15765	43	8	features	feature	NOUN
fcis-15765	43	9	through	through	ADP
fcis-15765	43	10	feature	feature	NOUN
fcis-15765	43	11	correspondence	correspondence	NOUN
fcis-15765	43	12	and	and	CCONJ
fcis-15765	43	13	geometric	geometric	ADJ
fcis-15765	43	14	constraints	constraint	NOUN
fcis-15765	43	15	between	between	ADP
fcis-15765	43	16	image	image	NOUN
fcis-15765	43	17	sequences	sequence	NOUN
fcis-15765	43	18	,	,	PUNCT
fcis-15765	43	19	i.e.	i.e.	X
fcis-15765	43	20	,	,	PUNCT
fcis-15765	43	21	the	the	DET
fcis-15765	43	22	accuracy	accuracy	NOUN
fcis-15765	43	23	of	of	ADP
fcis-15765	43	24	depth	depth	NOUN
fcis-15765	43	25	estimation	estimation	NOUN
fcis-15765	43	26	largely	largely	ADV
fcis-15765	43	27	depends	depend	VERB
fcis-15765	43	28	on	on	ADP
fcis-15765	43	29	precise	precise	ADJ
fcis-15765	43	30	feature	feature	NOUN
fcis-15765	43	31	matching	matching	NOUN
fcis-15765	43	32	and	and	CCONJ
fcis-15765	43	33	high	high	ADJ
fcis-15765	43	34	-	-	PUNCT
fcis-15765	43	35	quality	quality	NOUN
fcis-15765	43	36	image	image	NOUN
fcis-15765	43	37	sequences	sequence	NOUN
fcis-15765	43	38	.	.	PUNCT
fcis-15765	44	1	in	in	ADP
fcis-15765	44	2	addition	addition	NOUN
fcis-15765	44	3	,	,	PUNCT
fcis-15765	44	4	sfm	sfm	PROPN
fcis-15765	44	5	suffers	suffer	VERB
fcis-15765	44	6	from	from	ADP
fcis-15765	44	7	the	the	DET
fcis-15765	44	8	problem	problem	NOUN
fcis-15765	44	9	of	of	ADP
fcis-15765	44	10	monocular	monocular	ADJ
fcis-15765	44	11	scale	scale	NOUN
fcis-15765	44	12	ambiguity	ambiguity	NOUN
fcis-15765	44	13	[	[	X
fcis-15765	44	14	18	18	NUM
fcis-15765	44	15	]	]	PUNCT
fcis-15765	44	16	.	.	PUNCT
fcis-15765	45	1	likewise	likewise	ADV
fcis-15765	45	2	,	,	PUNCT
fcis-15765	45	3	stereo	stereo	ADJ
fcis-15765	45	4	vision	vision	NOUN
fcis-15765	45	5	matching	matching	NOUN
fcis-15765	45	6	also	also	ADV
fcis-15765	45	7	has	have	VERB
fcis-15765	45	8	the	the	DET
fcis-15765	45	9	ability	ability	NOUN
fcis-15765	45	10	to	to	PART
fcis-15765	45	11	recover	recover	VERB
fcis-15765	45	12	the	the	DET
fcis-15765	45	13	3d	3d	NUM
fcis-15765	45	14	structure	structure	NOUN
fcis-15765	45	15	of	of	ADP
fcis-15765	45	16	the	the	DET
fcis-15765	45	17	scene	scene	NOUN
fcis-15765	45	18	from	from	ADP
fcis-15765	45	19	two	two	NUM
fcis-15765	45	20	viewpoints	viewpoint	NOUN
fcis-15765	45	21	[	[	X
fcis-15765	45	22	19][20	19][20	X
fcis-15765	45	23	]	]	X
fcis-15765	45	24	viewing	view	VERB
fcis-15765	45	25	the	the	DET
fcis-15765	45	26	scene	scene	NOUN
fcis-15765	45	27	.	.	PUNCT
fcis-15765	46	1	stereo	stereo	PROPN
fcis-15765	46	2	vision	vision	PROPN
fcis-15765	46	3	matching	matching	NOUN
fcis-15765	46	4	uses	use	VERB
fcis-15765	46	5	two	two	NUM
fcis-15765	46	6	cameras	camera	NOUN
fcis-15765	46	7	to	to	PART
fcis-15765	46	8	simulate	simulate	VERB
fcis-15765	46	9	the	the	DET
fcis-15765	46	10	human	human	ADJ
fcis-15765	46	11	eye	eye	NOUN
fcis-15765	46	12	,	,	PUNCT
fcis-15765	46	13	and	and	CCONJ
fcis-15765	46	14	calculates	calculate	VERB
fcis-15765	46	15	the	the	DET
fcis-15765	46	16	disparity	disparity	NOUN
fcis-15765	46	17	map	map	NOUN
fcis-15765	46	18	of	of	ADP
fcis-15765	46	19	the	the	DET
fcis-15765	46	20	image	image	NOUN
fcis-15765	46	21	through	through	ADP
fcis-15765	46	22	a	a	DET
fcis-15765	46	23	cost	cost	NOUN
fcis-15765	46	24	function	function	NOUN
fcis-15765	46	25	.	.	PUNCT
fcis-15765	47	1	unlike	unlike	ADP
fcis-15765	47	2	the	the	DET
fcis-15765	47	3	sfm	sfm	PROPN
fcis-15765	47	4	process	process	NOUN
fcis-15765	47	5	based	base	VERB
fcis-15765	47	6	on	on	ADP
fcis-15765	47	7	monocular	monocular	ADJ
fcis-15765	47	8	sequences	sequence	NOUN
fcis-15765	47	9	[	[	X
fcis-15765	47	10	21][22	21][22	NOUN
fcis-15765	47	11	]	]	PUNCT
fcis-15765	47	12	,	,	PUNCT
fcis-15765	47	13	scale	scale	NOUN
fcis-15765	47	14	information	information	NOUN
fcis-15765	47	15	is	be	AUX
fcis-15765	47	16	included	include	VERB
fcis-15765	47	17	in	in	ADP
fcis-15765	47	18	the	the	DET
fcis-15765	47	19	depth	depth	NOUN
fcis-15765	47	20	estimation	estimation	NOUN
fcis-15765	47	21	during	during	ADP
fcis-15765	47	22	stereo	stereo	PROPN
fcis-15765	47	23	vision	vision	PROPN
fcis-15765	47	24	matching	matching	NOUN
fcis-15765	47	25	,	,	PUNCT
fcis-15765	47	26	since	since	SCONJ
fcis-15765	47	27	the	the	DET
fcis-15765	47	28	29	29	NUM
fcis-15765	47	29	transformation	transformation	NOUN
fcis-15765	47	30	between	between	ADP
fcis-15765	47	31	the	the	DET
fcis-15765	47	32	two	two	NUM
fcis-15765	47	33	cameras	camera	NOUN
fcis-15765	47	34	is	be	AUX
fcis-15765	47	35	calibrated	calibrate	VERB
fcis-15765	47	36	in	in	ADP
fcis-15765	47	37	advance	advance	NOUN
fcis-15765	47	38	.	.	PUNCT
fcis-15765	48	1	although	although	SCONJ
fcis-15765	48	2	the	the	DET
fcis-15765	48	3	above	above	ADJ
fcis-15765	48	4	geometry	geometry	NOUN
fcis-15765	48	5	-	-	PUNCT
fcis-15765	48	6	based	base	VERB
fcis-15765	48	7	methods	method	NOUN
fcis-15765	48	8	can	can	AUX
fcis-15765	48	9	efficiently	efficiently	ADV
fcis-15765	48	10	compute	compute	VERB
fcis-15765	48	11	the	the	DET
fcis-15765	48	12	depth	depth	NOUN
fcis-15765	48	13	values	value	NOUN
fcis-15765	48	14	of	of	ADP
fcis-15765	48	15	sparse	sparse	ADJ
fcis-15765	48	16	points	point	NOUN
fcis-15765	48	17	,	,	PUNCT
fcis-15765	48	18	these	these	DET
fcis-15765	48	19	methods	method	NOUN
fcis-15765	48	20	usually	usually	ADV
fcis-15765	48	21	rely	rely	VERB
fcis-15765	48	22	on	on	ADP
fcis-15765	48	23	image	image	NOUN
fcis-15765	48	24	pairs	pair	NOUN
fcis-15765	48	25	or	or	CCONJ
fcis-15765	48	26	image	image	NOUN
fcis-15765	48	27	sequences	sequence	NOUN
fcis-15765	48	28	[	[	X
fcis-15765	48	29	16][20	16][20	X
fcis-15765	48	30	]	]	PUNCT
fcis-15765	48	31	.	.	PUNCT
fcis-15765	49	1	how	how	SCONJ
fcis-15765	49	2	to	to	PART
fcis-15765	49	3	obtain	obtain	VERB
fcis-15765	49	4	dense	dense	ADJ
fcis-15765	49	5	depth	depth	NOUN
fcis-15765	49	6	maps	map	NOUN
fcis-15765	49	7	from	from	ADP
fcis-15765	49	8	a	a	DET
fcis-15765	49	9	single	single	ADJ
fcis-15765	49	10	image	image	NOUN
fcis-15765	49	11	is	be	AUX
fcis-15765	49	12	still	still	ADV
fcis-15765	49	13	a	a	DET
fcis-15765	49	14	major	major	ADJ
fcis-15765	49	15	challenge	challenge	NOUN
fcis-15765	49	16	due	due	ADP
fcis-15765	49	17	to	to	ADP
fcis-15765	49	18	the	the	DET
fcis-15765	49	19	lack	lack	NOUN
fcis-15765	49	20	of	of	ADP
fcis-15765	49	21	effective	effective	ADJ
fcis-15765	49	22	geometric	geometric	ADJ
fcis-15765	49	23	solutions	solution	NOUN
fcis-15765	49	24	.	.	PUNCT
fcis-15765	50	1	unlike	unlike	ADP
fcis-15765	50	2	the	the	DET
fcis-15765	50	3	geometric	geometric	ADJ
fcis-15765	50	4	estimation	estimation	NOUN
fcis-15765	50	5	method	method	NOUN
fcis-15765	50	6	,	,	PUNCT
fcis-15765	50	7	the	the	DET
fcis-15765	50	8	depth	depth	NOUN
fcis-15765	50	9	sensor	sensor	NOUN
fcis-15765	50	10	detection	detection	NOUN
fcis-15765	50	11	method	method	NOUN
fcis-15765	50	12	can	can	AUX
fcis-15765	50	13	directly	directly	ADV
fcis-15765	50	14	acquire	acquire	VERB
fcis-15765	50	15	the	the	DET
fcis-15765	50	16	scene	scene	NOUN
fcis-15765	50	17	without	without	ADP
fcis-15765	50	18	complex	complex	ADJ
fcis-15765	50	19	calculations	calculation	NOUN
fcis-15765	50	20	,	,	PUNCT
fcis-15765	50	21	but	but	CCONJ
fcis-15765	50	22	it	it	PRON
fcis-15765	50	23	is	be	AUX
fcis-15765	50	24	expensive	expensive	ADJ
fcis-15765	50	25	and	and	CCONJ
fcis-15765	50	26	easily	easily	ADV
fcis-15765	50	27	affected	affect	VERB
fcis-15765	50	28	by	by	ADP
fcis-15765	50	29	environmental	environmental	ADJ
fcis-15765	50	30	factors	factor	NOUN
fcis-15765	50	31	.	.	PUNCT
fcis-15765	51	1	like	like	INTJ
fcis-15765	51	2	ultrasound	ultrasound	NOUN
fcis-15765	51	3	and	and	CCONJ
fcis-15765	51	4	tof	tof	NOUN
fcis-15765	51	5	for	for	ADP
fcis-15765	51	6	measuring	measure	VERB
fcis-15765	51	7	depth	depth	NOUN
fcis-15765	51	8	,	,	PUNCT
fcis-15765	51	9	the	the	DET
fcis-15765	51	10	known	know	VERB
fcis-15765	51	11	velocity	velocity	NOUN
fcis-15765	51	12	of	of	ADP
fcis-15765	51	13	the	the	DET
fcis-15765	51	14	wave	wave	NOUN
fcis-15765	51	15	is	be	AUX
fcis-15765	51	16	used	use	VERB
fcis-15765	51	17	to	to	PART
fcis-15765	51	18	measure	measure	VERB
fcis-15765	51	19	the	the	DET
fcis-15765	51	20	time	time	NOUN
fcis-15765	51	21	it	it	PRON
fcis-15765	51	22	takes	take	VERB
fcis-15765	51	23	for	for	SCONJ
fcis-15765	51	24	the	the	DET
fcis-15765	51	25	transmitted	transmit	VERB
fcis-15765	51	26	pulse	pulse	NOUN
fcis-15765	51	27	to	to	PART
fcis-15765	51	28	reach	reach	VERB
fcis-15765	51	29	the	the	DET
fcis-15765	51	30	image	image	NOUN
fcis-15765	51	31	sensor	sensor	NOUN
fcis-15765	51	32	.	.	PUNCT
fcis-15765	52	1	rgb	rgb	PROPN
fcis-15765	52	2	-	-	PROPN
fcis-15765	52	3	d	d	PROPN
fcis-15765	52	4	cameras	camera	NOUN
fcis-15765	52	5	and	and	CCONJ
fcis-15765	52	6	lidar	lidar	NOUN
fcis-15765	52	7	can	can	AUX
fcis-15765	52	8	directly	directly	ADV
fcis-15765	52	9	obtain	obtain	VERB
fcis-15765	52	10	the	the	DET
fcis-15765	52	11	depth	depth	NOUN
fcis-15765	52	12	information	information	NOUN
fcis-15765	52	13	of	of	ADP
fcis-15765	52	14	the	the	DET
fcis-15765	52	15	corresponding	corresponding	ADJ
fcis-15765	52	16	image	image	NOUN
fcis-15765	52	17	.	.	PUNCT
fcis-15765	53	1	rgb	rgb	PROPN
fcis-15765	53	2	-	-	PROPN
fcis-15765	53	3	d	d	PROPN
fcis-15765	53	4	cameras	camera	NOUN
fcis-15765	53	5	have	have	VERB
fcis-15765	53	6	the	the	DET
fcis-15765	53	7	ability	ability	NOUN
fcis-15765	53	8	to	to	PART
fcis-15765	53	9	directly	directly	ADV
fcis-15765	53	10	acquire	acquire	VERB
fcis-15765	53	11	pixellevel	pixellevel	ADJ
fcis-15765	53	12	dense	dense	ADJ
fcis-15765	53	13	depth	depth	NOUN
fcis-15765	53	14	maps	map	NOUN
fcis-15765	53	15	of	of	ADP
fcis-15765	53	16	rgb	rgb	PROPN
fcis-15765	53	17	images	image	NOUN
fcis-15765	53	18	,	,	PUNCT
fcis-15765	53	19	but	but	CCONJ
fcis-15765	53	20	their	their	PRON
fcis-15765	53	21	measurement	measurement	NOUN
fcis-15765	53	22	range	range	NOUN
fcis-15765	53	23	is	be	AUX
fcis-15765	53	24	limited	limit	VERB
fcis-15765	53	25	and	and	CCONJ
fcis-15765	53	26	they	they	PRON
fcis-15765	53	27	are	be	AUX
fcis-15765	53	28	sensitive	sensitive	ADJ
fcis-15765	53	29	to	to	ADP
fcis-15765	53	30	outdoor	outdoor	ADJ
fcis-15765	53	31	sunlight	sunlight	NOUN
fcis-15765	53	32	[	[	X
fcis-15765	53	33	23	23	NUM
fcis-15765	53	34	]	]	PUNCT
fcis-15765	53	35	.	.	PUNCT
fcis-15765	54	1	although	although	SCONJ
fcis-15765	54	2	lidar	lidar	NOUN
fcis-15765	54	3	is	be	AUX
fcis-15765	54	4	widely	widely	ADV
fcis-15765	54	5	used	use	VERB
fcis-15765	54	6	in	in	ADP
fcis-15765	54	7	the	the	DET
fcis-15765	54	8	autonomous	autonomous	ADJ
fcis-15765	54	9	driving	drive	VERB
fcis-15765	54	10	industry	industry	NOUN
fcis-15765	54	11	for	for	ADP
fcis-15765	54	12	depth	depth	NOUN
fcis-15765	54	13	measurement	measurement	NOUN
fcis-15765	54	14	[	[	X
fcis-15765	54	15	24	24	NUM
fcis-15765	54	16	]	]	PUNCT
fcis-15765	54	17	,	,	PUNCT
fcis-15765	54	18	it	it	PRON
fcis-15765	54	19	can	can	AUX
fcis-15765	54	20	only	only	ADV
fcis-15765	54	21	generate	generate	VERB
fcis-15765	54	22	sparse	sparse	ADJ
fcis-15765	54	23	3d	3d	NUM
fcis-15765	54	24	maps	map	NOUN
fcis-15765	54	25	.	.	PUNCT
fcis-15765	55	1	furthermore	furthermore	ADV
fcis-15765	55	2	,	,	PUNCT
fcis-15765	55	3	the	the	DET
fcis-15765	55	4	large	large	ADJ
fcis-15765	55	5	size	size	NOUN
fcis-15765	55	6	and	and	CCONJ
fcis-15765	55	7	power	power	NOUN
fcis-15765	55	8	consumption	consumption	NOUN
fcis-15765	55	9	of	of	ADP
fcis-15765	55	10	these	these	DET
fcis-15765	55	11	depth	depth	NOUN
fcis-15765	55	12	sensors	sensor	NOUN
fcis-15765	55	13	(	(	PUNCT
fcis-15765	55	14	rgb	rgb	PROPN
fcis-15765	55	15	-	-	PROPN
fcis-15765	55	16	d	d	PROPN
fcis-15765	55	17	cameras	camera	NOUN
fcis-15765	55	18	and	and	CCONJ
fcis-15765	55	19	lidar	lidar	NOUN
fcis-15765	55	20	)	)	PUNCT
fcis-15765	55	21	hinder	hinder	VERB
fcis-15765	55	22	their	their	PRON
fcis-15765	55	23	use	use	NOUN
fcis-15765	55	24	in	in	ADP
fcis-15765	55	25	small	small	ADJ
fcis-15765	55	26	robots	robot	NOUN
fcis-15765	55	27	such	such	ADJ
fcis-15765	55	28	as	as	ADP
fcis-15765	55	29	drones	drone	NOUN
fcis-15765	55	30	.	.	PUNCT
fcis-15765	56	1	due	due	ADP
fcis-15765	56	2	to	to	ADP
fcis-15765	56	3	the	the	DET
fcis-15765	56	4	low	low	ADJ
fcis-15765	56	5	cost	cost	NOUN
fcis-15765	56	6	,	,	PUNCT
fcis-15765	56	7	small	small	ADJ
fcis-15765	56	8	size	size	NOUN
fcis-15765	56	9	and	and	CCONJ
fcis-15765	56	10	wide	wide	ADJ
fcis-15765	56	11	application	application	NOUN
fcis-15765	56	12	of	of	ADP
fcis-15765	56	13	monocular	monocular	ADJ
fcis-15765	56	14	cameras	camera	NOUN
fcis-15765	56	15	,	,	PUNCT
fcis-15765	56	16	estimating	estimate	VERB
fcis-15765	56	17	dense	dense	ADJ
fcis-15765	56	18	depth	depth	NOUN
fcis-15765	56	19	maps	map	NOUN
fcis-15765	56	20	from	from	ADP
fcis-15765	56	21	a	a	DET
fcis-15765	56	22	single	single	ADJ
fcis-15765	56	23	image	image	NOUN
fcis-15765	56	24	has	have	AUX
fcis-15765	56	25	received	receive	VERB
fcis-15765	56	26	more	more	ADJ
fcis-15765	56	27	attention	attention	NOUN
fcis-15765	56	28	and	and	CCONJ
fcis-15765	56	29	has	have	AUX
fcis-15765	56	30	recently	recently	ADV
fcis-15765	56	31	been	be	AUX
fcis-15765	56	32	well	well	ADV
fcis-15765	56	33	studied	study	VERB
fcis-15765	56	34	in	in	ADP
fcis-15765	56	35	an	an	DET
fcis-15765	56	36	end	end	NOUN
fcis-15765	56	37	-	-	PUNCT
fcis-15765	56	38	to	to	ADP
fcis-15765	56	39	-	-	PUNCT
fcis-15765	56	40	end	end	NOUN
fcis-15765	56	41	manner	manner	NOUN
fcis-15765	56	42	based	base	VERB
fcis-15765	56	43	on	on	ADP
fcis-15765	56	44	deep	deep	ADJ
fcis-15765	56	45	learning	learning	NOUN
fcis-15765	56	46	.	.	PUNCT
fcis-15765	57	1	traditional	traditional	ADJ
fcis-15765	57	2	depth	depth	NOUN
fcis-15765	57	3	estimation	estimation	NOUN
fcis-15765	57	4	methods	method	NOUN
fcis-15765	57	5	suffer	suffer	VERB
fcis-15765	57	6	from	from	ADP
fcis-15765	57	7	various	various	ADJ
fcis-15765	57	8	limitations	limitation	NOUN
fcis-15765	57	9	,	,	PUNCT
fcis-15765	57	10	including	include	VERB
fcis-15765	57	11	computational	computational	ADJ
fcis-15765	57	12	complexity	complexity	NOUN
fcis-15765	57	13	and	and	CCONJ
fcis-15765	57	14	associated	associate	VERB
fcis-15765	57	15	high	high	ADJ
fcis-15765	57	16	energy	energy	NOUN
fcis-15765	57	17	consumption	consumption	NOUN
fcis-15765	57	18	requirements	requirement	NOUN
fcis-15765	57	19	.	.	PUNCT
fcis-15765	58	1	the	the	DET
fcis-15765	58	2	current	current	ADJ
fcis-15765	58	3	research	research	NOUN
fcis-15765	58	4	work	work	NOUN
fcis-15765	58	5	utilizes	utilize	VERB
fcis-15765	58	6	deep	deep	ADJ
fcis-15765	58	7	learning	learning	NOUN
fcis-15765	58	8	methods	method	NOUN
fcis-15765	58	9	to	to	PART
fcis-15765	58	10	achieve	achieve	VERB
fcis-15765	58	11	more	more	ADV
fcis-15765	58	12	accurate	accurate	ADJ
fcis-15765	58	13	results	result	NOUN
fcis-15765	58	14	with	with	ADP
fcis-15765	58	15	lower	low	ADJ
fcis-15765	58	16	computational	computational	ADJ
fcis-15765	58	17	and	and	CCONJ
fcis-15765	58	18	energy	energy	NOUN
fcis-15765	58	19	requirements	requirement	NOUN
fcis-15765	58	20	.	.	PUNCT
fcis-15765	59	1	the	the	DET
fcis-15765	59	2	availability	availability	NOUN
fcis-15765	59	3	of	of	ADP
fcis-15765	59	4	deep	deep	ADJ
fcis-15765	59	5	learning	learning	NOUN
fcis-15765	59	6	-	-	PUNCT
fcis-15765	59	7	based	base	VERB
fcis-15765	59	8	methods	method	NOUN
fcis-15765	59	9	and	and	CCONJ
fcis-15765	59	10	large	large	ADJ
fcis-15765	59	11	-	-	PUNCT
fcis-15765	59	12	scale	scale	NOUN
fcis-15765	59	13	datasets	dataset	NOUN
fcis-15765	59	14	has	have	AUX
fcis-15765	59	15	greatly	greatly	ADV
fcis-15765	59	16	changed	change	VERB
fcis-15765	59	17	monocular	monocular	ADJ
fcis-15765	59	18	depth	depth	NOUN
fcis-15765	59	19	estimation	estimation	NOUN
fcis-15765	59	20	methods	method	NOUN
fcis-15765	59	21	.	.	PUNCT
fcis-15765	60	1	3	3	X
fcis-15765	60	2	.	.	X
fcis-15765	60	3	datasets	dataset	NOUN
fcis-15765	60	4	and	and	CCONJ
fcis-15765	60	5	evaluation	evaluation	NOUN
fcis-15765	60	6	metrics	metric	NOUN
fcis-15765	60	7	in	in	ADP
fcis-15765	60	8	depth	depth	NOUN
fcis-15765	60	9	estimation	estimation	NOUN
fcis-15765	60	10	3.1	3.1	NUM
fcis-15765	60	11	.	.	PUNCT
fcis-15765	61	1	datasets	dataset	NOUN
fcis-15765	61	2	deep	deep	ADJ
fcis-15765	61	3	learning	learning	NOUN
fcis-15765	61	4	is	be	AUX
fcis-15765	61	5	a	a	DET
fcis-15765	61	6	machine	machine	NOUN
fcis-15765	61	7	learning	learning	NOUN
fcis-15765	61	8	method	method	NOUN
fcis-15765	61	9	that	that	PRON
fcis-15765	61	10	relies	rely	VERB
fcis-15765	61	11	on	on	ADP
fcis-15765	61	12	large	large	ADJ
fcis-15765	61	13	-	-	PUNCT
fcis-15765	61	14	scale	scale	NOUN
fcis-15765	61	15	data	datum	NOUN
fcis-15765	61	16	sets	set	NOUN
fcis-15765	61	17	for	for	ADP
fcis-15765	61	18	training	training	NOUN
fcis-15765	61	19	.	.	PUNCT
fcis-15765	62	1	a	a	DET
fcis-15765	62	2	dataset	dataset	NOUN
fcis-15765	62	3	of	of	ADP
fcis-15765	62	4	sufficient	sufficient	ADJ
fcis-15765	62	5	size	size	NOUN
fcis-15765	62	6	and	and	CCONJ
fcis-15765	62	7	quality	quality	NOUN
fcis-15765	62	8	is	be	AUX
fcis-15765	62	9	critical	critical	ADJ
fcis-15765	62	10	to	to	ADP
fcis-15765	62	11	the	the	DET
fcis-15765	62	12	performance	performance	NOUN
fcis-15765	62	13	of	of	ADP
fcis-15765	62	14	deep	deep	ADJ
fcis-15765	62	15	learning	learning	NOUN
fcis-15765	62	16	models	model	NOUN
fcis-15765	62	17	.	.	PUNCT
fcis-15765	63	1	an	an	DET
fcis-15765	63	2	ideal	ideal	ADJ
fcis-15765	63	3	training	training	NOUN
fcis-15765	63	4	set	set	NOUN
fcis-15765	63	5	should	should	AUX
fcis-15765	63	6	contain	contain	VERB
fcis-15765	63	7	diverse	diverse	ADJ
fcis-15765	63	8	and	and	CCONJ
fcis-15765	63	9	representative	representative	ADJ
fcis-15765	63	10	samples	sample	NOUN
fcis-15765	63	11	to	to	PART
fcis-15765	63	12	capture	capture	VERB
fcis-15765	63	13	patterns	pattern	NOUN
fcis-15765	63	14	in	in	ADP
fcis-15765	63	15	data	datum	NOUN
fcis-15765	63	16	and	and	CCONJ
fcis-15765	63	17	generalize	generalize	VERB
fcis-15765	63	18	them	they	PRON
fcis-15765	63	19	to	to	ADP
fcis-15765	63	20	new	new	ADJ
fcis-15765	63	21	data	datum	NOUN
fcis-15765	63	22	.	.	PUNCT
fcis-15765	64	1	unlike	unlike	ADP
fcis-15765	64	2	traditional	traditional	ADJ
fcis-15765	64	3	machine	machine	NOUN
fcis-15765	64	4	learning	learning	NOUN
fcis-15765	64	5	methods	method	NOUN
fcis-15765	64	6	,	,	PUNCT
fcis-15765	64	7	deep	deep	ADJ
fcis-15765	64	8	learning	learning	NOUN
fcis-15765	64	9	can	can	AUX
fcis-15765	64	10	automatically	automatically	ADV
fcis-15765	64	11	learn	learn	VERB
fcis-15765	64	12	feature	feature	NOUN
fcis-15765	64	13	representations	representation	NOUN
fcis-15765	64	14	directly	directly	ADV
fcis-15765	64	15	from	from	ADP
fcis-15765	64	16	raw	raw	ADJ
fcis-15765	64	17	data	datum	NOUN
fcis-15765	64	18	without	without	ADP
fcis-15765	64	19	manual	manual	ADJ
fcis-15765	64	20	feature	feature	NOUN
fcis-15765	64	21	engineering	engineering	NOUN
fcis-15765	64	22	.	.	PUNCT
fcis-15765	65	1	large	large	ADJ
fcis-15765	65	2	data	datum	NOUN
fcis-15765	65	3	sets	set	NOUN
fcis-15765	65	4	enable	enable	VERB
fcis-15765	65	5	deep	deep	ADJ
fcis-15765	65	6	learning	learning	NOUN
fcis-15765	65	7	models	model	NOUN
fcis-15765	65	8	to	to	PART
fcis-15765	65	9	learn	learn	VERB
fcis-15765	65	10	hierarchical	hierarchical	ADJ
fcis-15765	65	11	feature	feature	NOUN
fcis-15765	65	12	representations	representation	NOUN
fcis-15765	65	13	,	,	PUNCT
fcis-15765	65	14	from	from	ADP
fcis-15765	65	15	low	low	ADJ
fcis-15765	65	16	-	-	PUNCT
fcis-15765	65	17	level	level	NOUN
fcis-15765	65	18	edge	edge	NOUN
fcis-15765	65	19	and	and	CCONJ
fcis-15765	65	20	texture	texture	NOUN
fcis-15765	65	21	features	feature	NOUN
fcis-15765	65	22	to	to	ADP
fcis-15765	65	23	high	high	ADJ
fcis-15765	65	24	-	-	PUNCT
fcis-15765	65	25	level	level	NOUN
fcis-15765	65	26	semantic	semantic	ADJ
fcis-15765	65	27	concepts	concept	NOUN
fcis-15765	65	28	.	.	PUNCT
fcis-15765	66	1	high	high	ADJ
fcis-15765	66	2	-	-	PUNCT
fcis-15765	66	3	quality	quality	NOUN
fcis-15765	66	4	datasets	dataset	NOUN
fcis-15765	66	5	are	be	AUX
fcis-15765	66	6	the	the	DET
fcis-15765	66	7	basis	basis	NOUN
fcis-15765	66	8	for	for	ADP
fcis-15765	66	9	training	train	VERB
fcis-15765	66	10	deep	deep	ADJ
fcis-15765	66	11	learning	learning	NOUN
fcis-15765	66	12	models	model	NOUN
fcis-15765	66	13	for	for	ADP
fcis-15765	66	14	depth	depth	NOUN
fcis-15765	66	15	estimation	estimation	NOUN
fcis-15765	66	16	.	.	PUNCT
fcis-15765	67	1	containing	contain	VERB
fcis-15765	67	2	a	a	DET
fcis-15765	67	3	large	large	ADJ
fcis-15765	67	4	amount	amount	NOUN
fcis-15765	67	5	of	of	ADP
fcis-15765	67	6	accurate	accurate	ADJ
fcis-15765	67	7	depth	depth	NOUN
fcis-15765	67	8	annotation	annotation	NOUN
fcis-15765	67	9	data	datum	NOUN
fcis-15765	67	10	can	can	AUX
fcis-15765	67	11	capture	capture	VERB
fcis-15765	67	12	the	the	DET
fcis-15765	67	13	spatial	spatial	ADJ
fcis-15765	67	14	information	information	NOUN
fcis-15765	67	15	of	of	ADP
fcis-15765	67	16	the	the	DET
fcis-15765	67	17	scene	scene	NOUN
fcis-15765	67	18	and	and	CCONJ
fcis-15765	67	19	provide	provide	VERB
fcis-15765	67	20	rich	rich	ADJ
fcis-15765	67	21	supervision	supervision	NOUN
fcis-15765	67	22	signals	signal	NOUN
fcis-15765	67	23	for	for	ADP
fcis-15765	67	24	the	the	DET
fcis-15765	67	25	network	network	NOUN
fcis-15765	67	26	.	.	PUNCT
fcis-15765	68	1	the	the	DET
fcis-15765	68	2	selection	selection	NOUN
fcis-15765	68	3	of	of	ADP
fcis-15765	68	4	data	datum	NOUN
fcis-15765	68	5	sets	set	NOUN
fcis-15765	68	6	will	will	AUX
fcis-15765	68	7	also	also	ADV
fcis-15765	68	8	affect	affect	VERB
fcis-15765	68	9	the	the	DET
fcis-15765	68	10	scope	scope	NOUN
fcis-15765	68	11	of	of	ADP
fcis-15765	68	12	application	application	NOUN
fcis-15765	68	13	of	of	ADP
fcis-15765	68	14	the	the	DET
fcis-15765	68	15	method	method	NOUN
fcis-15765	68	16	,	,	PUNCT
fcis-15765	68	17	such	such	ADJ
fcis-15765	68	18	as	as	ADP
fcis-15765	68	19	indoor	indoor	ADJ
fcis-15765	68	20	scenes	scene	NOUN
fcis-15765	68	21	,	,	PUNCT
fcis-15765	68	22	unmanned	unmanned	ADJ
fcis-15765	68	23	driving	driving	NOUN
fcis-15765	68	24	,	,	PUNCT
fcis-15765	68	25	augmented	augment	VERB
fcis-15765	68	26	reality	reality	NOUN
fcis-15765	68	27	,	,	PUNCT
fcis-15765	68	28	etc	etc	X
fcis-15765	68	29	.	.	X
fcis-15765	68	30	therefore	therefore	ADV
fcis-15765	68	31	,	,	PUNCT
fcis-15765	68	32	in	in	ADP
fcis-15765	68	33	the	the	DET
fcis-15765	68	34	depth	depth	NOUN
fcis-15765	68	35	estimation	estimation	NOUN
fcis-15765	68	36	model	model	NOUN
fcis-15765	68	37	,	,	PUNCT
fcis-15765	68	38	different	different	ADJ
fcis-15765	68	39	data	data	NOUN
fcis-15765	68	40	sets	set	NOUN
fcis-15765	68	41	are	be	AUX
fcis-15765	68	42	selected	select	VERB
fcis-15765	68	43	or	or	CCONJ
fcis-15765	68	44	constructed	construct	VERB
fcis-15765	68	45	according	accord	VERB
fcis-15765	68	46	to	to	ADP
fcis-15765	68	47	different	different	ADJ
fcis-15765	68	48	scenarios	scenario	NOUN
fcis-15765	68	49	for	for	ADP
fcis-15765	68	50	model	model	NOUN
fcis-15765	68	51	training	training	NOUN
fcis-15765	68	52	.	.	PUNCT
fcis-15765	69	1	in	in	ADP
fcis-15765	69	2	this	this	DET
fcis-15765	69	3	paper	paper	NOUN
fcis-15765	69	4	,	,	PUNCT
fcis-15765	69	5	several	several	ADJ
fcis-15765	69	6	commonly	commonly	ADV
fcis-15765	69	7	used	use	VERB
fcis-15765	69	8	models	model	NOUN
fcis-15765	69	9	for	for	ADP
fcis-15765	69	10	depth	depth	NOUN
fcis-15765	69	11	estimation	estimation	NOUN
fcis-15765	69	12	are	be	AUX
fcis-15765	69	13	introduced	introduce	VERB
fcis-15765	69	14	.	.	PUNCT
fcis-15765	70	1	nyu	nyu	NOUN
fcis-15765	70	2	-	-	PUNCT
fcis-15765	70	3	depth	depth	NOUN
fcis-15765	70	4	v2	v2	NOUN
fcis-15765	70	5	.	.	PUNCT
fcis-15765	71	1	the	the	DET
fcis-15765	71	2	nyu	nyu	PROPN
fcis-15765	71	3	depth	depth	NOUN
fcis-15765	71	4	v2	v2	NOUN
fcis-15765	71	5	data	datum	NOUN
fcis-15765	71	6	set	set	VERB
fcis-15765	71	7	[	[	X
fcis-15765	71	8	25	25	NUM
fcis-15765	71	9	]	]	PUNCT
fcis-15765	71	10	consists	consist	VERB
fcis-15765	71	11	of	of	ADP
fcis-15765	71	12	120k	120k	NUM
fcis-15765	71	13	pairs	pair	NOUN
fcis-15765	71	14	of	of	ADP
fcis-15765	71	15	rgb	rgb	PROPN
fcis-15765	71	16	and	and	CCONJ
fcis-15765	71	17	depth	depth	NOUN
fcis-15765	71	18	images	image	NOUN
fcis-15765	71	19	.	.	PUNCT
fcis-15765	72	1	the	the	DET
fcis-15765	72	2	data	datum	NOUN
fcis-15765	72	3	set	set	VERB
fcis-15765	72	4	uses	use	VERB
fcis-15765	72	5	the	the	DET
fcis-15765	72	6	microsoft	microsoft	PROPN
fcis-15765	72	7	kinect	kinect	PROPN
fcis-15765	72	8	sensor	sensor	PROPN
fcis-15765	72	9	to	to	PART
fcis-15765	72	10	collect	collect	VERB
fcis-15765	72	11	464	464	NUM
fcis-15765	72	12	indoor	indoor	ADJ
fcis-15765	72	13	scenes	scene	NOUN
fcis-15765	72	14	,	,	PUNCT
fcis-15765	72	15	and	and	CCONJ
fcis-15765	72	16	splits	split	VERB
fcis-15765	72	17	the	the	DET
fcis-15765	72	18	indoor	indoor	ADJ
fcis-15765	72	19	scenes	scene	NOUN
fcis-15765	72	20	into	into	ADP
fcis-15765	72	21	249	249	NUM
fcis-15765	72	22	for	for	ADP
fcis-15765	72	23	training	training	NOUN
fcis-15765	72	24	.	.	PUNCT
fcis-15765	73	1	215	215	NUM
fcis-15765	73	2	for	for	ADP
fcis-15765	73	3	testing	testing	NOUN
fcis-15765	73	4	.	.	PUNCT
fcis-15765	74	1	the	the	DET
fcis-15765	74	2	rgb	rgb	PROPN
fcis-15765	74	3	image	image	NOUN
fcis-15765	74	4	resolution	resolution	NOUN
fcis-15765	74	5	in	in	ADP
fcis-15765	74	6	the	the	DET
fcis-15765	74	7	sequence	sequence	NOUN
fcis-15765	74	8	is	be	AUX
fcis-15765	74	9	640	640	NUM
fcis-15765	74	10	×	×	NOUN
fcis-15765	74	11	480	480	NUM
fcis-15765	74	12	pixels	pixel	NOUN
fcis-15765	74	13	.	.	PUNCT
fcis-15765	75	1	furthermore	furthermore	ADV
fcis-15765	75	2	,	,	PUNCT
fcis-15765	75	3	the	the	DET
fcis-15765	75	4	diversity	diversity	NOUN
fcis-15765	75	5	of	of	ADP
fcis-15765	75	6	lighting	lighting	NOUN
fcis-15765	75	7	conditions	condition	NOUN
fcis-15765	75	8	and	and	CCONJ
fcis-15765	75	9	scene	scene	NOUN
fcis-15765	75	10	categories	category	NOUN
fcis-15765	75	11	makes	make	VERB
fcis-15765	75	12	this	this	PRON
fcis-15765	75	13	dataset	dataset	NOUN
fcis-15765	75	14	broadly	broadly	ADV
fcis-15765	75	15	representative	representative	ADJ
fcis-15765	75	16	for	for	ADP
fcis-15765	75	17	realworld	realworld	PROPN
fcis-15765	75	18	applications	application	NOUN
fcis-15765	75	19	[	[	X
fcis-15765	75	20	26	26	NUM
fcis-15765	75	21	]	]	PUNCT
fcis-15765	75	22	.	.	PUNCT
fcis-15765	76	1	this	this	PRON
fcis-15765	76	2	improves	improve	VERB
fcis-15765	76	3	the	the	DET
fcis-15765	76	4	transferability	transferability	NOUN
fcis-15765	76	5	of	of	ADP
fcis-15765	76	6	research	research	NOUN
fcis-15765	76	7	results	result	NOUN
fcis-15765	76	8	to	to	ADP
fcis-15765	76	9	real	real	ADJ
fcis-15765	76	10	environments	environment	NOUN
fcis-15765	76	11	.	.	PUNCT
fcis-15765	77	1	it	it	PRON
fcis-15765	77	2	is	be	AUX
fcis-15765	77	3	worth	worth	ADJ
fcis-15765	77	4	mentioning	mention	VERB
fcis-15765	77	5	that	that	SCONJ
fcis-15765	77	6	the	the	DET
fcis-15765	77	7	nyu	nyu	ADJ
fcis-15765	77	8	-	-	PUNCT
fcis-15765	77	9	depth	depth	NOUN
fcis-15765	77	10	v2	v2	NOUN
fcis-15765	77	11	data	datum	NOUN
fcis-15765	77	12	set	set	VERB
fcis-15765	77	13	is	be	AUX
fcis-15765	77	14	publicly	publicly	ADV
fcis-15765	77	15	available	available	ADJ
fcis-15765	77	16	,	,	PUNCT
fcis-15765	77	17	and	and	CCONJ
fcis-15765	77	18	researchers	researcher	NOUN
fcis-15765	77	19	can	can	AUX
fcis-15765	77	20	compare	compare	VERB
fcis-15765	77	21	algorithms	algorithm	NOUN
fcis-15765	77	22	based	base	VERB
fcis-15765	77	23	on	on	ADP
fcis-15765	77	24	a	a	DET
fcis-15765	77	25	unified	unified	ADJ
fcis-15765	77	26	data	data	NOUN
fcis-15765	77	27	set	set	VERB
fcis-15765	77	28	,	,	PUNCT
fcis-15765	77	29	which	which	PRON
fcis-15765	77	30	accelerates	accelerate	VERB
fcis-15765	77	31	research	research	NOUN
fcis-15765	77	32	progress	progress	NOUN
fcis-15765	77	33	.	.	PUNCT
fcis-15765	78	1	overall	overall	ADV
fcis-15765	78	2	,	,	PUNCT
fcis-15765	78	3	nyudepth	nyudepth	ADJ
fcis-15765	78	4	v2	v2	PROPN
fcis-15765	78	5	has	have	AUX
fcis-15765	78	6	become	become	VERB
fcis-15765	78	7	an	an	DET
fcis-15765	78	8	important	important	ADJ
fcis-15765	78	9	evaluation	evaluation	NOUN
fcis-15765	78	10	benchmark	benchmark	NOUN
fcis-15765	78	11	and	and	CCONJ
fcis-15765	78	12	empirical	empirical	ADJ
fcis-15765	78	13	research	research	NOUN
fcis-15765	78	14	platform	platform	NOUN
fcis-15765	78	15	in	in	ADP
fcis-15765	78	16	rgb	rgb	PROPN
fcis-15765	78	17	-	-	PROPN
fcis-15765	78	18	d	d	PROPN
fcis-15765	78	19	scene	scene	NOUN
fcis-15765	78	20	analysis	analysis	NOUN
fcis-15765	78	21	,	,	PUNCT
fcis-15765	78	22	and	and	CCONJ
fcis-15765	78	23	it	it	PRON
fcis-15765	78	24	is	be	AUX
fcis-15765	78	25	now	now	ADV
fcis-15765	78	26	a	a	DET
fcis-15765	78	27	commonly	commonly	ADV
fcis-15765	78	28	used	use	VERB
fcis-15765	78	29	benchmark	benchmark	NOUN
fcis-15765	78	30	and	and	CCONJ
fcis-15765	78	31	main	main	ADJ
fcis-15765	78	32	training	training	NOUN
fcis-15765	78	33	dataset	dataset	NOUN
fcis-15765	78	34	in	in	ADP
fcis-15765	78	35	supervised	supervised	ADJ
fcis-15765	78	36	monocular	monocular	ADJ
fcis-15765	78	37	depth	depth	NOUN
fcis-15765	78	38	estimation	estimation	NOUN
fcis-15765	78	39	.	.	PUNCT
fcis-15765	79	1	kitti	kitti	PROPN
fcis-15765	79	2	.	.	PUNCT
fcis-15765	80	1	the	the	DET
fcis-15765	80	2	kitti	kitti	PROPN
fcis-15765	80	3	dataset	dataset	PROPN
fcis-15765	80	4	was	be	AUX
fcis-15765	80	5	released	release	VERB
fcis-15765	80	6	by	by	ADP
fcis-15765	80	7	karlsruhe	karlsruhe	PROPN
fcis-15765	80	8	institute	institute	PROPN
fcis-15765	80	9	of	of	ADP
fcis-15765	80	10	technology	technology	NOUN
fcis-15765	80	11	in	in	ADP
fcis-15765	80	12	2012	2012	NUM
fcis-15765	80	13	.	.	PUNCT
fcis-15765	81	1	[	[	X
fcis-15765	81	2	27	27	NUM
fcis-15765	81	3	]	]	PUNCT
fcis-15765	81	4	contains	contain	VERB
fcis-15765	81	5	image	image	NOUN
fcis-15765	81	6	sequences	sequence	NOUN
fcis-15765	81	7	and	and	CCONJ
fcis-15765	81	8	annotation	annotation	NOUN
fcis-15765	81	9	data	datum	NOUN
fcis-15765	81	10	in	in	ADP
fcis-15765	81	11	various	various	ADJ
fcis-15765	81	12	driving	drive	VERB
fcis-15765	81	13	scenarios	scenario	NOUN
fcis-15765	81	14	.	.	PUNCT
fcis-15765	82	1	the	the	DET
fcis-15765	82	2	resolution	resolution	NOUN
fcis-15765	82	3	of	of	ADP
fcis-15765	82	4	the	the	DET
fcis-15765	82	5	collected	collect	VERB
fcis-15765	82	6	images	image	NOUN
fcis-15765	82	7	is	be	AUX
fcis-15765	82	8	1242	1242	NUM
fcis-15765	82	9	×	×	NOUN
fcis-15765	82	10	375	375	NUM
fcis-15765	82	11	pixels	pixel	NOUN
fcis-15765	82	12	.	.	PUNCT
fcis-15765	83	1	the	the	DET
fcis-15765	83	2	data	datum	NOUN
fcis-15765	83	3	set	set	NOUN
fcis-15765	83	4	has	have	VERB
fcis-15765	83	5	the	the	DET
fcis-15765	83	6	following	following	ADJ
fcis-15765	83	7	characteristics	characteristic	NOUN
fcis-15765	83	8	:	:	PUNCT
fcis-15765	83	9	first	first	ADV
fcis-15765	83	10	,	,	PUNCT
fcis-15765	83	11	it	it	PRON
fcis-15765	83	12	has	have	VERB
fcis-15765	83	13	a	a	DET
fcis-15765	83	14	large	large	ADJ
fcis-15765	83	15	amount	amount	NOUN
fcis-15765	83	16	of	of	ADP
fcis-15765	83	17	data	datum	NOUN
fcis-15765	83	18	,	,	PUNCT
fcis-15765	83	19	including	include	VERB
fcis-15765	83	20	as	as	ADV
fcis-15765	83	21	many	many	ADJ
fcis-15765	83	22	as	as	ADP
fcis-15765	83	23	150,000	150,000	NUM
fcis-15765	83	24	highresolution	highresolution	NOUN
fcis-15765	83	25	images	image	NOUN
fcis-15765	83	26	and	and	CCONJ
fcis-15765	83	27	related	relate	VERB
fcis-15765	83	28	3d	3d	NUM
fcis-15765	83	29	object	object	NOUN
fcis-15765	83	30	detection	detection	NOUN
fcis-15765	83	31	,	,	PUNCT
fcis-15765	83	32	optical	optical	ADJ
fcis-15765	83	33	flow	flow	NOUN
fcis-15765	83	34	,	,	PUNCT
fcis-15765	83	35	parallax	parallax	NOUN
fcis-15765	83	36	,	,	PUNCT
fcis-15765	83	37	semantic	semantic	ADJ
fcis-15765	83	38	segmentation	segmentation	NOUN
fcis-15765	83	39	and	and	CCONJ
fcis-15765	83	40	other	other	ADJ
fcis-15765	83	41	rich	rich	ADJ
fcis-15765	83	42	annotations	annotation	NOUN
fcis-15765	83	43	[	[	X
fcis-15765	83	44	28	28	NUM
fcis-15765	83	45	]	]	X
fcis-15765	83	46	;	;	PUNCT
fcis-15765	83	47	second	second	X
fcis-15765	83	48	,	,	PUNCT
fcis-15765	83	49	the	the	DET
fcis-15765	83	50	data	data	NOUN
fcis-15765	83	51	is	be	AUX
fcis-15765	83	52	diverse	diverse	ADJ
fcis-15765	83	53	,	,	PUNCT
fcis-15765	83	54	including	include	VERB
fcis-15765	83	55	complex	complex	ADJ
fcis-15765	83	56	dynamic	dynamic	ADJ
fcis-15765	83	57	environments	environment	NOUN
fcis-15765	83	58	such	such	ADJ
fcis-15765	83	59	as	as	ADP
fcis-15765	83	60	cities	city	NOUN
fcis-15765	83	61	,	,	PUNCT
fcis-15765	83	62	villages	village	NOUN
fcis-15765	83	63	,	,	PUNCT
fcis-15765	83	64	and	and	CCONJ
fcis-15765	83	65	highways	highway	NOUN
fcis-15765	83	66	;	;	PUNCT
fcis-15765	83	67	in	in	ADP
fcis-15765	83	68	addition	addition	NOUN
fcis-15765	83	69	,	,	PUNCT
fcis-15765	83	70	the	the	DET
fcis-15765	83	71	selected	select	VERB
fcis-15765	83	72	driving	driving	NOUN
fcis-15765	83	73	scenarios	scenario	NOUN
fcis-15765	83	74	are	be	AUX
fcis-15765	83	75	close	close	ADJ
fcis-15765	83	76	to	to	ADP
fcis-15765	83	77	real	real	ADJ
fcis-15765	83	78	applications	application	NOUN
fcis-15765	83	79	and	and	CCONJ
fcis-15765	83	80	are	be	AUX
fcis-15765	83	81	representative	representative	ADJ
fcis-15765	83	82	[	[	X
fcis-15765	83	83	29	29	NUM
fcis-15765	83	84	]	]	PUNCT
fcis-15765	83	85	.	.	PUNCT
fcis-15765	84	1	in	in	ADP
fcis-15765	84	2	a	a	DET
fcis-15765	84	3	follow	follow	VERB
fcis-15765	84	4	-	-	PUNCT
fcis-15765	84	5	up	up	ADP
fcis-15765	84	6	study	study	NOUN
fcis-15765	84	7	,	,	PUNCT
fcis-15765	84	8	eigen	eigen	PROPN
fcis-15765	84	9	et	et	PROPN
fcis-15765	84	10	al	al	PROPN
fcis-15765	84	11	.	.	PUNCT
fcis-15765	85	1	[	[	X
fcis-15765	85	2	10	10	NUM
fcis-15765	85	3	]	]	PUNCT
fcis-15765	85	4	divided	divide	VERB
fcis-15765	85	5	the	the	DET
fcis-15765	85	6	56	56	NUM
fcis-15765	85	7	scenes	scene	NOUN
fcis-15765	85	8	in	in	ADP
fcis-15765	85	9	the	the	DET
fcis-15765	85	10	kitti	kitti	PROPN
fcis-15765	85	11	dataset	dataset	VERB
fcis-15765	85	12	into	into	ADP
fcis-15765	85	13	two	two	NUM
fcis-15765	85	14	parts	part	NOUN
fcis-15765	85	15	,	,	PUNCT
fcis-15765	85	16	28	28	NUM
fcis-15765	85	17	for	for	ADP
fcis-15765	85	18	training	training	NOUN
fcis-15765	85	19	and	and	CCONJ
fcis-15765	85	20	28	28	NUM
fcis-15765	85	21	for	for	ADP
fcis-15765	85	22	testing	testing	NOUN
fcis-15765	85	23	.	.	PUNCT
fcis-15765	86	1	each	each	DET
fcis-15765	86	2	scene	scene	NOUN
fcis-15765	86	3	consists	consist	VERB
fcis-15765	86	4	of	of	ADP
fcis-15765	86	5	stereoscopic	stereoscopic	ADJ
fcis-15765	86	6	image	image	NOUN
fcis-15765	86	7	pairs	pair	NOUN
fcis-15765	86	8	with	with	ADP
fcis-15765	86	9	a	a	DET
fcis-15765	86	10	resolution	resolution	NOUN
fcis-15765	86	11	of	of	ADP
fcis-15765	86	12	1224	1224	NUM
fcis-15765	86	13	×	×	NOUN
fcis-15765	86	14	368	368	NUM
fcis-15765	86	15	.	.	PUNCT
fcis-15765	87	1	based	base	VERB
fcis-15765	87	2	on	on	ADP
fcis-15765	87	3	the	the	DET
fcis-15765	87	4	above	above	ADJ
fcis-15765	87	5	advantages	advantage	NOUN
fcis-15765	87	6	,	,	PUNCT
fcis-15765	87	7	the	the	DET
fcis-15765	87	8	kitti	kitti	PROPN
fcis-15765	87	9	dataset	dataset	PROPN
fcis-15765	87	10	has	have	AUX
fcis-15765	87	11	been	be	AUX
fcis-15765	87	12	widely	widely	ADV
fcis-15765	87	13	used	use	VERB
fcis-15765	87	14	in	in	ADP
fcis-15765	87	15	multiple	multiple	ADJ
fcis-15765	87	16	important	important	ADJ
fcis-15765	87	17	tasks	task	NOUN
fcis-15765	87	18	such	such	ADJ
fcis-15765	87	19	as	as	ADP
fcis-15765	87	20	stereo	stereo	ADJ
fcis-15765	87	21	vision	vision	NOUN
fcis-15765	87	22	,	,	PUNCT
fcis-15765	87	23	object	object	NOUN
fcis-15765	87	24	detection	detection	NOUN
fcis-15765	87	25	,	,	PUNCT
fcis-15765	87	26	motion	motion	NOUN
fcis-15765	87	27	estimation	estimation	NOUN
fcis-15765	87	28	,	,	PUNCT
fcis-15765	87	29	and	and	CCONJ
fcis-15765	87	30	semantic	semantic	ADJ
fcis-15765	87	31	understanding	understanding	NOUN
fcis-15765	87	32	,	,	PUNCT
fcis-15765	87	33	and	and	CCONJ
fcis-15765	87	34	has	have	AUX
fcis-15765	87	35	promoted	promote	VERB
fcis-15765	87	36	research	research	NOUN
fcis-15765	87	37	progress	progress	NOUN
fcis-15765	87	38	in	in	ADP
fcis-15765	87	39	their	their	PRON
fcis-15765	87	40	respective	respective	ADJ
fcis-15765	87	41	fields	field	NOUN
fcis-15765	87	42	[	[	X
fcis-15765	87	43	30	30	NUM
fcis-15765	87	44	]	]	PUNCT
fcis-15765	87	45	.	.	PUNCT
fcis-15765	88	1	a	a	DET
fcis-15765	88	2	typical	typical	ADJ
fcis-15765	88	3	example	example	NOUN
fcis-15765	88	4	is	be	AUX
fcis-15765	88	5	that	that	SCONJ
fcis-15765	88	6	it	it	PRON
fcis-15765	88	7	promotes	promote	VERB
fcis-15765	88	8	the	the	DET
fcis-15765	88	9	development	development	NOUN
fcis-15765	88	10	of	of	ADP
fcis-15765	88	11	monocular	monocular	ADJ
fcis-15765	88	12	depth	depth	NOUN
fcis-15765	88	13	estimation	estimation	NOUN
fcis-15765	88	14	technology	technology	NOUN
fcis-15765	88	15	based	base	VERB
fcis-15765	88	16	on	on	ADP
fcis-15765	88	17	deep	deep	ADJ
fcis-15765	88	18	learning	learning	NOUN
fcis-15765	88	19	.	.	PUNCT
fcis-15765	89	1	therefore	therefore	ADV
fcis-15765	89	2	this	this	DET
fcis-15765	89	3	dataset	dataset	NOUN
fcis-15765	89	4	is	be	AUX
fcis-15765	89	5	the	the	DET
fcis-15765	89	6	most	most	ADV
fcis-15765	89	7	common	common	ADJ
fcis-15765	89	8	benchmark	benchmark	NOUN
fcis-15765	89	9	and	and	CCONJ
fcis-15765	89	10	main	main	ADJ
fcis-15765	89	11	training	training	NOUN
fcis-15765	89	12	dataset	dataset	NOUN
fcis-15765	89	13	in	in	ADP
fcis-15765	89	14	unsupervised	unsupervised	ADJ
fcis-15765	89	15	and	and	CCONJ
fcis-15765	89	16	semi	semi	ADJ
fcis-15765	89	17	-	-	ADJ
fcis-15765	89	18	supervised	supervised	ADJ
fcis-15765	89	19	monocular	monocular	ADJ
fcis-15765	89	20	depth	depth	NOUN
fcis-15765	89	21	estimation	estimation	NOUN
fcis-15765	89	22	.	.	PUNCT
fcis-15765	90	1	make3d	make3d	PRON
fcis-15765	90	2	the	the	DET
fcis-15765	90	3	make3d	make3d	PRON
fcis-15765	90	4	dataset	dataset	NOUN
fcis-15765	90	5	was	be	AUX
fcis-15765	90	6	originally	originally	ADV
fcis-15765	90	7	proposed	propose	VERB
fcis-15765	90	8	by	by	ADP
fcis-15765	90	9	columbia	columbia	PROPN
fcis-15765	90	10	university	university	PROPN
fcis-15765	90	11	in	in	ADP
fcis-15765	90	12	2009	2009	NUM
fcis-15765	90	13	,	,	PUNCT
fcis-15765	90	14	which	which	PRON
fcis-15765	90	15	contains	contain	VERB
fcis-15765	90	16	rgb	rgb	PROPN
fcis-15765	90	17	images	image	NOUN
fcis-15765	90	18	generated	generate	VERB
fcis-15765	90	19	by	by	ADP
fcis-15765	90	20	image	image	NOUN
fcis-15765	90	21	synthesis	synthesis	NOUN
fcis-15765	90	22	and	and	CCONJ
fcis-15765	90	23	corresponding	correspond	VERB
fcis-15765	90	24	depth	depth	NOUN
fcis-15765	90	25	maps	map	NOUN
fcis-15765	90	26	[	[	X
fcis-15765	90	27	31	31	NUM
fcis-15765	90	28	]	]	PUNCT
fcis-15765	90	29	.	.	PUNCT
fcis-15765	91	1	the	the	DET
fcis-15765	91	2	dataset	dataset	NOUN
fcis-15765	91	3	has	have	VERB
fcis-15765	91	4	the	the	DET
fcis-15765	91	5	following	follow	VERB
fcis-15765	91	6	salient	salient	NOUN
fcis-15765	91	7	features	feature	NOUN
fcis-15765	91	8	:	:	PUNCT
fcis-15765	91	9	first	first	ADV
fcis-15765	91	10	,	,	PUNCT
fcis-15765	91	11	the	the	DET
fcis-15765	91	12	scene	scene	NOUN
fcis-15765	91	13	is	be	AUX
fcis-15765	91	14	rich	rich	ADJ
fcis-15765	91	15	in	in	ADP
fcis-15765	91	16	themes	theme	NOUN
fcis-15765	91	17	,	,	PUNCT
fcis-15765	91	18	covering	cover	VERB
fcis-15765	91	19	outdoor	outdoor	ADJ
fcis-15765	91	20	and	and	CCONJ
fcis-15765	91	21	indoor	indoor	ADJ
fcis-15765	91	22	categories	category	NOUN
fcis-15765	91	23	;	;	PUNCT
fcis-15765	91	24	second	second	X
fcis-15765	91	25	,	,	PUNCT
fcis-15765	91	26	it	it	PRON
fcis-15765	91	27	has	have	VERB
fcis-15765	91	28	accurate	accurate	ADJ
fcis-15765	91	29	depth	depth	NOUN
fcis-15765	91	30	ground	ground	NOUN
fcis-15765	91	31	truth	truth	NOUN
fcis-15765	91	32	data	datum	NOUN
fcis-15765	91	33	;	;	PUNCT
fcis-15765	91	34	in	in	ADP
fcis-15765	91	35	addition	addition	NOUN
fcis-15765	91	36	,	,	PUNCT
fcis-15765	91	37	the	the	DET
fcis-15765	91	38	image	image	NOUN
fcis-15765	91	39	resolution	resolution	NOUN
fcis-15765	91	40	is	be	AUX
fcis-15765	91	41	high	high	ADJ
fcis-15765	91	42	and	and	CCONJ
fcis-15765	91	43	the	the	DET
fcis-15765	91	44	depth	depth	NOUN
fcis-15765	91	45	annotation	annotation	NOUN
fcis-15765	91	46	density	density	NOUN
fcis-15765	91	47	is	be	AUX
fcis-15765	91	48	high	high	ADJ
fcis-15765	91	49	.	.	PUNCT
fcis-15765	92	1	these	these	PRON
fcis-15765	92	2	make	make	VERB
fcis-15765	92	3	make3d	make3d	PRON
fcis-15765	92	4	a	a	DET
fcis-15765	92	5	high	high	ADJ
fcis-15765	92	6	-	-	PUNCT
fcis-15765	92	7	quality	quality	NOUN
fcis-15765	92	8	dataset	dataset	NOUN
fcis-15765	92	9	for	for	ADP
fcis-15765	92	10	early	early	ADJ
fcis-15765	92	11	learning	learning	NOUN
fcis-15765	92	12	of	of	ADP
fcis-15765	92	13	3d	3d	NUM
fcis-15765	92	14	scene	scene	NOUN
fcis-15765	92	15	representation	representation	NOUN
fcis-15765	92	16	.	.	PUNCT
fcis-15765	93	1	but	but	CCONJ
fcis-15765	93	2	the	the	DET
fcis-15765	93	3	make3d	make3d	DET
fcis-15765	93	4	dataset	dataset	NOUN
fcis-15765	94	1	[	[	X
fcis-15765	94	2	31	31	NUM
fcis-15765	94	3	]	]	PUNCT
fcis-15765	94	4	only	only	ADV
fcis-15765	94	5	contains	contain	VERB
fcis-15765	94	6	monocular	monocular	ADJ
fcis-15765	94	7	rgb	rgb	PROPN
fcis-15765	94	8	images	image	NOUN
fcis-15765	94	9	and	and	CCONJ
fcis-15765	94	10	depth	depth	NOUN
fcis-15765	94	11	images	image	NOUN
fcis-15765	94	12	,	,	PUNCT
fcis-15765	94	13	no	no	DET
fcis-15765	94	14	stereo	stereo	NOUN
fcis-15765	94	15	images	image	NOUN
fcis-15765	94	16	.	.	PUNCT
fcis-15765	95	1	since	since	SCONJ
fcis-15765	95	2	there	there	PRON
fcis-15765	95	3	are	be	VERB
fcis-15765	95	4	no	no	DET
fcis-15765	95	5	monocular	monocular	ADJ
fcis-15765	95	6	sequences	sequence	NOUN
fcis-15765	95	7	or	or	CCONJ
fcis-15765	95	8	stereo	stereo	NOUN
fcis-15765	95	9	image	image	NOUN
fcis-15765	95	10	pairs	pair	NOUN
fcis-15765	95	11	in	in	ADP
fcis-15765	95	12	this	this	DET
fcis-15765	95	13	dataset	dataset	NOUN
fcis-15765	95	14	,	,	PUNCT
fcis-15765	95	15	semi	semi	ADJ
fcis-15765	95	16	-	-	ADJ
fcis-15765	95	17	supervised	supervised	ADJ
fcis-15765	95	18	and	and	CCONJ
fcis-15765	95	19	unsupervised	unsupervised	ADJ
fcis-15765	95	20	learning	learning	NOUN
fcis-15765	95	21	methods	method	NOUN
fcis-15765	95	22	do	do	AUX
fcis-15765	95	23	not	not	PART
fcis-15765	95	24	use	use	VERB
fcis-15765	95	25	it	it	PRON
fcis-15765	95	26	as	as	ADP
fcis-15765	95	27	a	a	DET
fcis-15765	95	28	training	training	NOUN
fcis-15765	95	29	set	set	NOUN
fcis-15765	95	30	,	,	PUNCT
fcis-15765	95	31	while	while	SCONJ
fcis-15765	95	32	supervised	supervised	ADJ
fcis-15765	95	33	methods	method	NOUN
fcis-15765	95	34	usually	usually	ADV
fcis-15765	95	35	use	use	VERB
fcis-15765	95	36	it	it	PRON
fcis-15765	95	37	for	for	ADP
fcis-15765	95	38	training	training	NOUN
fcis-15765	95	39	.	.	PUNCT
fcis-15765	96	1	instead	instead	ADV
fcis-15765	96	2	,	,	PUNCT
fcis-15765	96	3	it	it	PRON
fcis-15765	96	4	is	be	AUX
fcis-15765	96	5	widely	widely	ADV
fcis-15765	96	6	used	use	VERB
fcis-15765	96	7	as	as	ADP
fcis-15765	96	8	a	a	DET
fcis-15765	96	9	test	test	NOUN
fcis-15765	96	10	set	set	VERB
fcis-15765	96	11	for	for	ADP
fcis-15765	96	12	unsupervised	unsupervised	ADJ
fcis-15765	96	13	algorithms	algorithm	NOUN
fcis-15765	96	14	to	to	PART
fcis-15765	96	15	evaluate	evaluate	VERB
fcis-15765	96	16	the	the	DET
fcis-15765	96	17	generalization	generalization	NOUN
fcis-15765	96	18	ability	ability	NOUN
fcis-15765	96	19	of	of	ADP
fcis-15765	96	20	the	the	DET
fcis-15765	96	21	network	network	NOUN
fcis-15765	96	22	on	on	ADP
fcis-15765	96	23	different	different	ADJ
fcis-15765	96	24	datasets	dataset	NOUN
fcis-15765	96	25	[	[	X
fcis-15765	96	26	32	32	NUM
fcis-15765	96	27	]	]	PUNCT
fcis-15765	96	28	.	.	PUNCT
fcis-15765	97	1	cityscapes	cityscape	NOUN
fcis-15765	97	2	the	the	DET
fcis-15765	97	3	cityscapes	cityscape	NOUN
fcis-15765	97	4	dataset	dataset	VERB
fcis-15765	97	5	[	[	X
fcis-15765	97	6	33	33	NUM
fcis-15765	97	7	]	]	PUNCT
fcis-15765	97	8	mainly	mainly	ADV
fcis-15765	97	9	focuses	focus	VERB
fcis-15765	97	10	on	on	ADP
fcis-15765	97	11	the	the	DET
fcis-15765	97	12	task	task	NOUN
fcis-15765	97	13	of	of	ADP
fcis-15765	97	14	semantic	semantic	ADJ
fcis-15765	97	15	segmentation	segmentation	NOUN
fcis-15765	97	16	.	.	PUNCT
fcis-15765	98	1	there	there	PRON
fcis-15765	98	2	are	be	VERB
fcis-15765	98	3	5000	5000	NUM
fcis-15765	98	4	finely	finely	ADV
fcis-15765	98	5	annotated	annotate	VERB
fcis-15765	98	6	images	image	NOUN
fcis-15765	98	7	and	and	CCONJ
fcis-15765	98	8	20000	20000	NUM
fcis-15765	98	9	coarsely	coarsely	ADV
fcis-15765	98	10	annotated	annotate	VERB
fcis-15765	98	11	images	image	NOUN
fcis-15765	98	12	in	in	ADP
fcis-15765	98	13	this	this	DET
fcis-15765	98	14	dataset	dataset	NOUN
fcis-15765	98	15	.	.	PUNCT
fcis-15765	99	1	since	since	SCONJ
fcis-15765	99	2	this	this	DET
fcis-15765	99	3	dataset	dataset	NOUN
fcis-15765	99	4	does	do	AUX
fcis-15765	99	5	not	not	PART
fcis-15765	99	6	contain	contain	VERB
fcis-15765	99	7	true	true	ADJ
fcis-15765	99	8	values	value	NOUN
fcis-15765	99	9	of	of	ADP
fcis-15765	99	10	depth	depth	NOUN
fcis-15765	99	11	,	,	PUNCT
fcis-15765	99	12	it	it	PRON
fcis-15765	99	13	is	be	AUX
fcis-15765	99	14	only	only	ADV
fcis-15765	99	15	applied	apply	VERB
fcis-15765	99	16	in	in	ADP
fcis-15765	99	17	the	the	DET
fcis-15765	99	18	training	training	NOUN
fcis-15765	99	19	process	process	NOUN
fcis-15765	99	20	of	of	ADP
fcis-15765	99	21	several	several	ADJ
fcis-15765	99	22	unsupervised	unsupervised	ADJ
fcis-15765	99	23	depth	depth	NOUN
fcis-15765	99	24	estimation	estimation	NOUN
fcis-15765	99	25	methods	method	NOUN
fcis-15765	99	26	[	[	X
fcis-15765	99	27	32	32	NUM
fcis-15765	99	28	]	]	PUNCT
fcis-15765	99	29	.	.	PUNCT
fcis-15765	100	1	at	at	ADP
fcis-15765	100	2	the	the	DET
fcis-15765	100	3	same	same	ADJ
fcis-15765	100	4	30	30	NUM
fcis-15765	100	5	time	time	NOUN
fcis-15765	100	6	,	,	PUNCT
fcis-15765	100	7	the	the	DET
fcis-15765	100	8	dataset	dataset	NOUN
fcis-15765	100	9	also	also	ADV
fcis-15765	100	10	includes	include	VERB
fcis-15765	100	11	binocular	binocular	ADJ
fcis-15765	100	12	video	video	NOUN
fcis-15765	100	13	sequences	sequence	NOUN
fcis-15765	100	14	of	of	ADP
fcis-15765	100	15	multiple	multiple	ADJ
fcis-15765	100	16	cities	city	NOUN
fcis-15765	100	17	in	in	ADP
fcis-15765	100	18	different	different	ADJ
fcis-15765	100	19	months	month	NOUN
fcis-15765	100	20	,	,	PUNCT
fcis-15765	100	21	which	which	PRON
fcis-15765	100	22	can	can	AUX
fcis-15765	100	23	provide	provide	VERB
fcis-15765	100	24	22973	22973	NUM
fcis-15765	100	25	pairs	pair	NOUN
fcis-15765	100	26	of	of	ADP
fcis-15765	100	27	images	image	NOUN
fcis-15765	100	28	for	for	ADP
fcis-15765	100	29	the	the	DET
fcis-15765	100	30	training	training	NOUN
fcis-15765	100	31	of	of	ADP
fcis-15765	100	32	the	the	DET
fcis-15765	100	33	unsupervised	unsupervised	ADJ
fcis-15765	100	34	monocular	monocular	ADJ
fcis-15765	100	35	depth	depth	NOUN
fcis-15765	100	36	estimation	estimation	NOUN
fcis-15765	100	37	model	model	NOUN
fcis-15765	100	38	.	.	PUNCT
fcis-15765	101	1	the	the	DET
fcis-15765	101	2	performance	performance	NOUN
fcis-15765	101	3	of	of	ADP
fcis-15765	101	4	the	the	DET
fcis-15765	101	5	deep	deep	ADJ
fcis-15765	101	6	network	network	NOUN
fcis-15765	101	7	is	be	AUX
fcis-15765	101	8	improved	improve	VERB
fcis-15765	101	9	by	by	ADP
fcis-15765	101	10	pre	pre	VERB
fcis-15765	101	11	-	-	NOUN
fcis-15765	101	12	training	train	VERB
fcis-15765	101	13	the	the	DET
fcis-15765	101	14	network	network	NOUN
fcis-15765	101	15	on	on	ADP
fcis-15765	101	16	cityscapes	cityscape	NOUN
fcis-15765	101	17	and	and	CCONJ
fcis-15765	101	18	subsequent	subsequent	ADJ
fcis-15765	101	19	training	training	NOUN
fcis-15765	101	20	on	on	ADP
fcis-15765	101	21	other	other	ADJ
fcis-15765	101	22	datasets	dataset	NOUN
fcis-15765	101	23	.	.	PUNCT
fcis-15765	102	1	3.2	3.2	NUM
fcis-15765	102	2	.	.	PUNCT
fcis-15765	102	3	evaluation	evaluation	NOUN
fcis-15765	102	4	metrics	metric	NOUN
fcis-15765	102	5	in	in	ADP
fcis-15765	102	6	order	order	NOUN
fcis-15765	102	7	to	to	PART
fcis-15765	102	8	measure	measure	VERB
fcis-15765	102	9	the	the	DET
fcis-15765	102	10	model	model	NOUN
fcis-15765	102	11	performance	performance	NOUN
fcis-15765	102	12	of	of	ADP
fcis-15765	102	13	monocular	monocular	ADJ
fcis-15765	102	14	depth	depth	NOUN
fcis-15765	102	15	estimation	estimation	NOUN
fcis-15765	102	16	,	,	PUNCT
fcis-15765	102	17	literature	literature	NOUN
fcis-15765	102	18	[	[	X
fcis-15765	102	19	10	10	NUM
fcis-15765	102	20	]	]	PUNCT
fcis-15765	102	21	provides	provide	VERB
fcis-15765	102	22	five	five	NUM
fcis-15765	102	23	indicators	indicator	NOUN
fcis-15765	102	24	,	,	PUNCT
fcis-15765	102	25	which	which	PRON
fcis-15765	102	26	are	be	AUX
fcis-15765	102	27	:	:	PUNCT
fcis-15765	102	28	rmse	rmse	ADJ
fcis-15765	102	29	,	,	PUNCT
fcis-15765	102	30	rmse	rmse	PROPN
fcis-15765	102	31	log	log	PROPN
fcis-15765	102	32	,	,	PUNCT
fcis-15765	102	33	abs	ab	NOUN
fcis-15765	102	34	rel	rel	NOUN
fcis-15765	102	35	,	,	PUNCT
fcis-15765	102	36	sq	sq	ADJ
fcis-15765	102	37	rel	rel	NOUN
fcis-15765	102	38	,	,	PUNCT
fcis-15765	102	39	accuracies	accuracy	NOUN
fcis-15765	102	40	,	,	PUNCT
fcis-15765	102	41	as	as	SCONJ
fcis-15765	102	42	follows	follow	VERB
fcis-15765	102	43	:	:	PUNCT
fcis-15765	102	44	the	the	DET
fcis-15765	102	45	linear	linear	PROPN
fcis-15765	102	46	root	root	NOUN
fcis-15765	102	47	mean	mean	VERB
fcis-15765	102	48	square	square	ADJ
fcis-15765	102	49	error	error	NOUN
fcis-15765	102	50	(	(	PUNCT
fcis-15765	102	51	rmse	rmse	NOUN
fcis-15765	102	52	):	):	PUNCT
fcis-15765	102	53	defined	define	VERB
fcis-15765	102	54	as	as	ADP
fcis-15765	102	55	:	:	PUNCT
fcis-15765	102	56	*	*	NUM
fcis-15765	102	57	21	21	NUM
fcis-15765	102	58	||	||	NOUN
fcis-15765	103	1	||	||	PUNCT
fcis-15765	104	1	|	|	ADV
fcis-15765	105	1	|	|	ADV
fcis-15765	105	2	i	i	PRON
fcis-15765	106	1	i	i	PRON
fcis-15765	106	2	i	i	PRON
fcis-15765	106	3	n	n	VERB
fcis-15765	106	4	rmse	rmse	VERB
fcis-15765	107	1	d	d	X
fcis-15765	107	2	d	d	PROPN
fcis-15765	107	3	n	n	PROPN
fcis-15765	107	4			NOUN
fcis-15765	107	5			PRON
fcis-15765	108	1			X
fcis-15765	108	2	(	(	PUNCT
fcis-15765	108	3	1	1	X
fcis-15765	108	4	)	)	PUNCT
fcis-15765	108	5	the	the	DET
fcis-15765	108	6	logarithm	logarithm	PROPN
fcis-15765	108	7	root	root	NOUN
fcis-15765	108	8	mean	mean	VERB
fcis-15765	108	9	square	square	ADJ
fcis-15765	108	10	error	error	NOUN
fcis-15765	108	11	(	(	PUNCT
fcis-15765	108	12	rmse	rmse	PROPN
fcis-15765	108	13	log	log	PROPN
fcis-15765	108	14	):	):	PUNCT
fcis-15765	108	15	defined	define	VERB
fcis-15765	108	16	as	as	ADP
fcis-15765	108	17	:	:	PUNCT
fcis-15765	108	18	*	*	NUM
fcis-15765	108	19	21	21	NUM
fcis-15765	108	20	log	log	NOUN
fcis-15765	108	21	||	||	NOUN
fcis-15765	108	22	log	log	PROPN
fcis-15765	108	23	(	(	PUNCT
fcis-15765	108	24	)	)	PUNCT
fcis-15765	108	25	log	log	NOUN
fcis-15765	108	26	(	(	PUNCT
fcis-15765	108	27	)	)	PUNCT
fcis-15765	108	28	||	||	PUNCT
fcis-15765	109	1	|	|	ADV
fcis-15765	110	1	|	|	ADV
fcis-15765	110	2	i	i	PRON
fcis-15765	111	1	i	i	PRON
fcis-15765	111	2	i	i	PRON
fcis-15765	111	3	n	n	VERB
fcis-15765	111	4	rmse	rmse	VERB
fcis-15765	112	1	d	d	X
fcis-15765	112	2	d	d	PROPN
fcis-15765	112	3	n	n	PROPN
fcis-15765	112	4			NOUN
fcis-15765	112	5			PRON
fcis-15765	113	1			X
fcis-15765	113	2	(	(	PUNCT
fcis-15765	113	3	2	2	NUM
fcis-15765	113	4	)	)	PUNCT
fcis-15765	113	5	absolute	absolute	ADJ
fcis-15765	113	6	relative	relative	ADJ
fcis-15765	113	7	difference	difference	NOUN
fcis-15765	113	8	(	(	PUNCT
fcis-15765	113	9	abs	ab	NOUN
fcis-15765	113	10	rel	rel	NOUN
fcis-15765	113	11	):	):	PUNCT
fcis-15765	113	12	defined	define	VERB
fcis-15765	113	13	as	as	ADP
fcis-15765	113	14	the	the	DET
fcis-15765	113	15	average	average	NOUN
fcis-15765	113	16	of	of	ADP
fcis-15765	113	17	the	the	DET
fcis-15765	113	18	l1	l1	PROPN
fcis-15765	113	19	distance	distance	NOUN
fcis-15765	113	20	between	between	ADP
fcis-15765	113	21	the	the	DET
fcis-15765	113	22	ground	ground	NOUN
fcis-15765	113	23	truth	truth	NOUN
fcis-15765	113	24	depth	depth	NOUN
fcis-15765	113	25	and	and	CCONJ
fcis-15765	113	26	the	the	DET
fcis-15765	113	27	estimated	estimate	VERB
fcis-15765	113	28	depth	depth	NOUN
fcis-15765	113	29	over	over	ADP
fcis-15765	113	30	all	all	DET
fcis-15765	113	31	image	image	NOUN
fcis-15765	113	32	pixels	pixel	NOUN
fcis-15765	113	33	,	,	PUNCT
fcis-15765	113	34	but	but	CCONJ
fcis-15765	113	35	on	on	ADP
fcis-15765	113	36	the	the	DET
fcis-15765	113	37	scale	scale	NOUN
fcis-15765	113	38	of	of	ADP
fcis-15765	113	39	the	the	DET
fcis-15765	113	40	estimated	estimate	VERB
fcis-15765	113	41	depth	depth	NOUN
fcis-15765	113	42	:	:	PUNCT
fcis-15765	114	1	*	*	PUNCT
fcis-15765	114	2	*	*	PUNCT
fcis-15765	115	1	|	|	INTJ
fcis-15765	115	2	|1	|1	PRON
fcis-15765	116	1	|	|	ADV
fcis-15765	117	1	|	|	ADV
fcis-15765	117	2	i	i	PRON
fcis-15765	118	1	i	i	PRON
fcis-15765	118	2	i	i	PRON
fcis-15765	119	1	n	n	VERB
fcis-15765	120	1	i	i	NOUN
fcis-15765	120	2	d	d	PROPN
fcis-15765	120	3	d	d	NOUN
fcis-15765	120	4	abs	ab	NOUN
fcis-15765	120	5	rel	rel	NOUN
fcis-15765	120	6	n	n	DET
fcis-15765	120	7	d	d	ADJ
fcis-15765	120	8			PROPN
fcis-15765	120	9			PRON
fcis-15765	120	10			X
fcis-15765	120	11	(	(	PUNCT
fcis-15765	120	12	3	3	X
fcis-15765	120	13	)	)	PUNCT
fcis-15765	120	14	squared	square	VERB
fcis-15765	120	15	relative	relative	ADJ
fcis-15765	120	16	difference	difference	NOUN
fcis-15765	120	17	(	(	PUNCT
fcis-15765	120	18	sq	sq	INTJ
fcis-15765	120	19	rel	rel	NOUN
fcis-15765	120	20	):	):	PUNCT
fcis-15765	120	21	defined	define	VERB
fcis-15765	120	22	as	as	ADP
fcis-15765	120	23	the	the	DET
fcis-15765	120	24	average	average	NOUN
fcis-15765	120	25	of	of	ADP
fcis-15765	120	26	the	the	DET
fcis-15765	120	27	l2	l2	NOUN
fcis-15765	120	28	distance	distance	NOUN
fcis-15765	120	29	between	between	ADP
fcis-15765	120	30	the	the	DET
fcis-15765	120	31	ground	ground	NOUN
fcis-15765	120	32	truth	truth	NOUN
fcis-15765	120	33	depth	depth	NOUN
fcis-15765	120	34	and	and	CCONJ
fcis-15765	120	35	the	the	DET
fcis-15765	120	36	estimated	estimate	VERB
fcis-15765	120	37	depth	depth	NOUN
fcis-15765	120	38	over	over	ADP
fcis-15765	120	39	all	all	DET
fcis-15765	120	40	image	image	NOUN
fcis-15765	120	41	pixels	pixel	NOUN
fcis-15765	120	42	,	,	PUNCT
fcis-15765	120	43	but	but	CCONJ
fcis-15765	120	44	scaled	scale	VERB
fcis-15765	120	45	by	by	ADP
fcis-15765	120	46	the	the	DET
fcis-15765	120	47	estimated	estimate	VERB
fcis-15765	120	48	depth	depth	NOUN
fcis-15765	120	49	:	:	PUNCT
fcis-15765	120	50	*	*	SYM
fcis-15765	120	51	2	2	X
fcis-15765	120	52	*	*	PUNCT
fcis-15765	120	53	||	||	NOUN
fcis-15765	120	54	||1	||1	NOUN
fcis-15765	121	1	|	|	ADV
fcis-15765	122	1	|	|	ADV
fcis-15765	122	2	i	i	PRON
fcis-15765	123	1	i	i	PRON
fcis-15765	123	2	i	i	PRON
fcis-15765	124	1	n	n	VERB
fcis-15765	124	2	i	i	NOUN
fcis-15765	124	3	d	d	NOUN
fcis-15765	124	4	d	d	X
fcis-15765	124	5	sq	sq	INTJ
fcis-15765	124	6	rel	rel	NOUN
fcis-15765	124	7	n	n	CCONJ
fcis-15765	124	8	d	d	ADJ
fcis-15765	124	9			PROPN
fcis-15765	124	10			PRON
fcis-15765	124	11			X
fcis-15765	124	12	(	(	PUNCT
fcis-15765	124	13	4	4	X
fcis-15765	124	14	)	)	PUNCT
fcis-15765	124	15	accuracy	accuracy	NOUN
fcis-15765	124	16	under	under	ADP
fcis-15765	124	17	a	a	DET
fcis-15765	124	18	threshold	threshold	NOUN
fcis-15765	124	19	:	:	PUNCT
fcis-15765	124	20	is	be	AUX
fcis-15765	124	21	the	the	DET
fcis-15765	124	22	percentage	percentage	NOUN
fcis-15765	124	23	of	of	ADP
fcis-15765	124	24	predicted	predict	VERB
fcis-15765	124	25	pixels	pixel	NOUN
fcis-15765	124	26	whose	whose	DET
fcis-15765	124	27	relative	relative	ADJ
fcis-15765	124	28	error	error	NOUN
fcis-15765	124	29	is	be	AUX
fcis-15765	124	30	within	within	ADP
fcis-15765	124	31	the	the	DET
fcis-15765	124	32	threshold	threshold	NOUN
fcis-15765	124	33	.	.	PUNCT
fcis-15765	125	1	the	the	DET
fcis-15765	125	2	formula	formula	NOUN
fcis-15765	125	3	is	be	AUX
fcis-15765	125	4	expressed	express	VERB
fcis-15765	125	5	as	as	ADP
fcis-15765	125	6	:	:	PUNCT
fcis-15765	125	7	*	*	PUNCT
fcis-15765	125	8	*	*	PUNCT
fcis-15765	125	9	:	:	PUNCT
fcis-15765	125	10	%	%	INTJ
fcis-15765	125	11	.	.	PUNCT
fcis-15765	125	12	.	.	PUNCT
fcis-15765	126	1	max	max	PROPN
fcis-15765	126	2	(	(	PUNCT
fcis-15765	126	3	,	,	PUNCT
fcis-15765	126	4	)	)	PUNCT
fcis-15765	127	1	i	i	PRON
fcis-15765	127	2	i	i	PRON
fcis-15765	128	1	i	i	PRON
fcis-15765	129	1	i	i	PRON
fcis-15765	129	2	i	i	VERB
fcis-15765	130	1	d	d	NOUN
fcis-15765	130	2	d	d	NOUN
fcis-15765	130	3	accuracies	accuracy	NOUN
fcis-15765	130	4	of	of	ADP
fcis-15765	130	5	d	d	PROPN
fcis-15765	130	6	s	s	PROPN
fcis-15765	130	7	t	t	X
fcis-15765	130	8	thr	thr	INTJ
fcis-15765	131	1	d	d	PROPN
fcis-15765	131	2	d	d	ADP
fcis-15765	131	3			PROPN
fcis-15765	131	4			PROPN
fcis-15765	131	5	(	(	PUNCT
fcis-15765	131	6	5	5	NUM
fcis-15765	131	7	)	)	PUNCT
fcis-15765	131	8	where	where	SCONJ
fcis-15765	131	9	is	be	AUX
fcis-15765	131	10	the	the	DET
fcis-15765	131	11	predicted	predict	VERB
fcis-15765	131	12	depth	depth	NOUN
fcis-15765	131	13	value	value	NOUN
fcis-15765	131	14	of	of	ADP
fcis-15765	131	15	pixel	pixel	PROPN
fcis-15765	131	16	i	i	PRON
fcis-15765	131	17	,	,	PUNCT
fcis-15765	131	18	and	and	CCONJ
fcis-15765	131	19	represents	represent	VERB
fcis-15765	131	20	the	the	DET
fcis-15765	131	21	true	true	ADJ
fcis-15765	131	22	value	value	NOUN
fcis-15765	131	23	of	of	ADP
fcis-15765	131	24	depth	depth	NOUN
fcis-15765	131	25	.	.	PUNCT
fcis-15765	132	1	in	in	ADP
fcis-15765	132	2	addition	addition	NOUN
fcis-15765	132	3	,	,	PUNCT
fcis-15765	132	4	n	n	PRON
fcis-15765	132	5	represents	represent	VERB
fcis-15765	132	6	the	the	DET
fcis-15765	132	7	total	total	ADJ
fcis-15765	132	8	number	number	NOUN
fcis-15765	132	9	of	of	ADP
fcis-15765	132	10	pixels	pixel	NOUN
fcis-15765	132	11	with	with	ADP
fcis-15765	132	12	real	real	ADJ
fcis-15765	132	13	depth	depth	NOUN
fcis-15765	132	14	values	value	NOUN
fcis-15765	132	15	,	,	PUNCT
fcis-15765	132	16	and	and	CCONJ
fcis-15765	132	17	thr	thr	PROPN
fcis-15765	132	18	represents	represent	VERB
fcis-15765	132	19	the	the	DET
fcis-15765	132	20	threshold	threshold	NOUN
fcis-15765	132	21	value	value	NOUN
fcis-15765	132	22	,	,	PUNCT
fcis-15765	132	23	usually	usually	ADV
fcis-15765	132	24	1.25	1.25	NUM
fcis-15765	132	25	,	,	PUNCT
fcis-15765	132	26	1.252	1.252	NUM
fcis-15765	132	27	,	,	PUNCT
fcis-15765	132	28	1.253	1.253	NUM
fcis-15765	132	29	.	.	PUNCT
fcis-15765	133	1	4	4	X
fcis-15765	133	2	.	.	X
fcis-15765	133	3	supervised	supervise	VERB
fcis-15765	133	4	monocular	monocular	ADJ
fcis-15765	133	5	depth	depth	NOUN
fcis-15765	133	6	estimation	estimation	NOUN
fcis-15765	133	7	if	if	SCONJ
fcis-15765	133	8	you	you	PRON
fcis-15765	133	9	follow	follow	VERB
fcis-15765	133	10	the	the	DET
fcis-15765	133	11	“	"	PUNCT
fcis-15765	133	12	checklist	checklist	NOUN
fcis-15765	133	13	”	"	PUNCT
fcis-15765	133	14	your	your	PRON
fcis-15765	133	15	paper	paper	NOUN
fcis-15765	133	16	will	will	AUX
fcis-15765	133	17	conform	conform	VERB
fcis-15765	133	18	to	to	ADP
fcis-15765	133	19	the	the	DET
fcis-15765	133	20	requirements	requirement	NOUN
fcis-15765	133	21	of	of	ADP
fcis-15765	133	22	the	the	DET
fcis-15765	133	23	publisher	publisher	NOUN
fcis-15765	133	24	and	and	CCONJ
fcis-15765	133	25	facilitate	facilitate	VERB
fcis-15765	133	26	a	a	DET
fcis-15765	133	27	problem	problem	NOUN
fcis-15765	133	28	-	-	PUNCT
fcis-15765	133	29	free	free	ADJ
fcis-15765	133	30	publication	publication	NOUN
fcis-15765	133	31	process	process	NOUN
fcis-15765	133	32	.	.	PUNCT
fcis-15765	134	1	the	the	DET
fcis-15765	134	2	supervision	supervision	NOUN
fcis-15765	134	3	signal	signal	NOUN
fcis-15765	134	4	of	of	ADP
fcis-15765	134	5	supervised	supervised	ADJ
fcis-15765	134	6	methods	method	NOUN
fcis-15765	134	7	is	be	AUX
fcis-15765	134	8	based	base	VERB
fcis-15765	134	9	on	on	ADP
fcis-15765	134	10	the	the	DET
fcis-15765	134	11	input	input	NOUN
fcis-15765	134	12	ground	ground	NOUN
fcis-15765	134	13	-	-	PUNCT
fcis-15765	134	14	truth	truth	NOUN
fcis-15765	134	15	depth	depth	NOUN
fcis-15765	134	16	map	map	NOUN
fcis-15765	134	17	,	,	PUNCT
fcis-15765	134	18	so	so	ADV
fcis-15765	134	19	monocular	monocular	ADJ
fcis-15765	134	20	depth	depth	NOUN
fcis-15765	134	21	estimation	estimation	NOUN
fcis-15765	134	22	can	can	AUX
fcis-15765	134	23	be	be	AUX
fcis-15765	134	24	regarded	regard	VERB
fcis-15765	134	25	as	as	ADP
fcis-15765	134	26	a	a	DET
fcis-15765	134	27	regression	regression	NOUN
fcis-15765	134	28	problem	problem	NOUN
fcis-15765	134	29	[	[	X
fcis-15765	134	30	31	31	NUM
fcis-15765	134	31	]	]	PUNCT
fcis-15765	134	32	.	.	PUNCT
fcis-15765	135	1	deep	deep	ADJ
fcis-15765	135	2	neural	neural	ADJ
fcis-15765	135	3	networks	network	NOUN
fcis-15765	135	4	aim	aim	VERB
fcis-15765	135	5	to	to	PART
fcis-15765	135	6	predict	predict	VERB
fcis-15765	135	7	a	a	DET
fcis-15765	135	8	depth	depth	NOUN
fcis-15765	135	9	map	map	NOUN
fcis-15765	135	10	from	from	ADP
fcis-15765	135	11	a	a	DET
fcis-15765	135	12	single	single	ADJ
fcis-15765	135	13	image	image	NOUN
fcis-15765	135	14	.	.	PUNCT
fcis-15765	136	1	using	use	VERB
fcis-15765	136	2	the	the	DET
fcis-15765	136	3	difference	difference	NOUN
fcis-15765	136	4	between	between	ADP
fcis-15765	136	5	the	the	DET
fcis-15765	136	6	predicted	predict	VERB
fcis-15765	136	7	depth	depth	NOUN
fcis-15765	136	8	map	map	NOUN
fcis-15765	136	9	and	and	CCONJ
fcis-15765	136	10	the	the	DET
fcis-15765	136	11	real	real	ADJ
fcis-15765	136	12	depth	depth	NOUN
fcis-15765	136	13	map	map	NOUN
fcis-15765	136	14	to	to	PART
fcis-15765	136	15	supervise	supervise	VERB
fcis-15765	136	16	the	the	DET
fcis-15765	136	17	training	training	NOUN
fcis-15765	136	18	of	of	ADP
fcis-15765	136	19	the	the	DET
fcis-15765	136	20	network	network	NOUN
fcis-15765	136	21	,	,	PUNCT
fcis-15765	136	22	the	the	DET
fcis-15765	136	23	l2	l2	NOUN
fcis-15765	136	24	loss	loss	NOUN
fcis-15765	136	25	formula	formula	NOUN
fcis-15765	136	26	is	be	AUX
fcis-15765	136	27	as	as	SCONJ
fcis-15765	136	28	follows	follow	VERB
fcis-15765	136	29	:	:	PUNCT
fcis-15765	136	30			PROPN
fcis-15765	136	31			PROPN
fcis-15765	136	32	*	*	SYM
fcis-15765	136	33	2	2	NUM
fcis-15765	136	34	*	*	SYM
fcis-15765	136	35	2	2	NUM
fcis-15765	136	36	2	2	NUM
fcis-15765	136	37	1	1	NUM
fcis-15765	136	38	,	,	PUNCT
fcis-15765	136	39	|	|	ADV
fcis-15765	137	1	|	|	ADV
fcis-15765	137	2	||	||	NOUN
fcis-15765	138	1	n	n	INTJ
fcis-15765	139	1	i	i	NOUN
fcis-15765	139	2	d	d	NOUN
fcis-15765	139	3	d	d	PROPN
fcis-15765	139	4	d	d	PROPN
fcis-15765	139	5	d	d	PROPN
fcis-15765	139	6	n	n	CCONJ
fcis-15765	139	7			NUM
fcis-15765	139	8			NOUN
fcis-15765	139	9	(	(	PUNCT
fcis-15765	139	10	6	6	NUM
fcis-15765	139	11	)	)	PUNCT
fcis-15765	139	12	therefore	therefore	ADV
fcis-15765	139	13	,	,	PUNCT
fcis-15765	139	14	the	the	DET
fcis-15765	139	15	deep	deep	ADJ
fcis-15765	139	16	network	network	NOUN
fcis-15765	139	17	learns	learn	VERB
fcis-15765	139	18	the	the	DET
fcis-15765	139	19	depth	depth	NOUN
fcis-15765	139	20	information	information	NOUN
fcis-15765	139	21	of	of	ADP
fcis-15765	139	22	the	the	DET
fcis-15765	139	23	scene	scene	NOUN
fcis-15765	139	24	by	by	ADP
fcis-15765	139	25	approximating	approximate	VERB
fcis-15765	139	26	the	the	DET
fcis-15765	139	27	ground	ground	NOUN
fcis-15765	139	28	truth	truth	NOUN
fcis-15765	139	29	.	.	PUNCT
fcis-15765	140	1	the	the	DET
fcis-15765	140	2	overall	overall	ADJ
fcis-15765	140	3	flow	flow	NOUN
fcis-15765	140	4	chart	chart	NOUN
fcis-15765	140	5	is	be	AUX
fcis-15765	140	6	as	as	SCONJ
fcis-15765	140	7	follows	follow	VERB
fcis-15765	140	8	:	:	PUNCT
fcis-15765	140	9	ground	ground	NOUN
fcis-15765	140	10	truth	truth	NOUN
fcis-15765	140	11	loss	loss	NOUN
fcis-15765	140	12	guided	guide	VERB
fcis-15765	140	13	training	training	NOUN
fcis-15765	140	14	depthnet	depthnet	NOUN
fcis-15765	140	15	output	output	NOUN
fcis-15765	140	16	deptninput	deptninput	VERB
fcis-15765	140	17	rgb	rgb	PROPN
fcis-15765	140	18	fig	fig	PROPN
fcis-15765	140	19	1	1	NUM
fcis-15765	140	20	.	.	PUNCT
fcis-15765	141	1	supervised	supervise	VERB
fcis-15765	141	2	basic	basic	ADJ
fcis-15765	141	3	models	model	NOUN
fcis-15765	141	4	to	to	ADP
fcis-15765	141	5	the	the	DET
fcis-15765	141	6	best	good	ADJ
fcis-15765	141	7	of	of	ADP
fcis-15765	141	8	our	our	PRON
fcis-15765	141	9	knowledge	knowledge	NOUN
fcis-15765	141	10	,	,	PUNCT
fcis-15765	141	11	eigen	eigen	PROPN
fcis-15765	141	12	et	et	PROPN
fcis-15765	141	13	al	al	PROPN
fcis-15765	141	14	.	.	PUNCT
fcis-15765	142	1	[	[	X
fcis-15765	142	2	10	10	NUM
fcis-15765	142	3	]	]	PUNCT
fcis-15765	142	4	were	be	AUX
fcis-15765	142	5	the	the	DET
fcis-15765	142	6	first	first	ADJ
fcis-15765	142	7	to	to	PART
fcis-15765	142	8	employ	employ	VERB
fcis-15765	142	9	a	a	DET
fcis-15765	142	10	cnn	cnn	NOUN
fcis-15765	142	11	architecture	architecture	NOUN
fcis-15765	142	12	for	for	ADP
fcis-15765	142	13	the	the	DET
fcis-15765	142	14	task	task	NOUN
fcis-15765	142	15	of	of	ADP
fcis-15765	142	16	monocular	monocular	ADJ
fcis-15765	142	17	depth	depth	NOUN
fcis-15765	142	18	estimation	estimation	NOUN
fcis-15765	142	19	consisting	consist	VERB
fcis-15765	142	20	of	of	ADP
fcis-15765	142	21	a	a	DET
fcis-15765	142	22	coarse	coarse	ADJ
fcis-15765	142	23	-	-	PUNCT
fcis-15765	142	24	scale	scale	NOUN
fcis-15765	142	25	network	network	NOUN
fcis-15765	142	26	that	that	PRON
fcis-15765	142	27	performs	perform	VERB
fcis-15765	142	28	global	global	ADJ
fcis-15765	142	29	predictions	prediction	NOUN
fcis-15765	142	30	and	and	CCONJ
fcis-15765	142	31	a	a	DET
fcis-15765	142	32	fine	fine	ADJ
fcis-15765	142	33	-	-	PUNCT
fcis-15765	142	34	scale	scale	NOUN
fcis-15765	142	35	network	network	NOUN
fcis-15765	142	36	that	that	PRON
fcis-15765	142	37	refines	refine	VERB
fcis-15765	142	38	predictions	prediction	NOUN
fcis-15765	142	39	locally	locally	ADV
fcis-15765	142	40	.	.	PUNCT
fcis-15765	143	1	then	then	ADV
fcis-15765	143	2	,	,	PUNCT
fcis-15765	143	3	they	they	PRON
fcis-15765	143	4	[	[	X
fcis-15765	143	5	38	38	NUM
fcis-15765	143	6	]	]	PUNCT
fcis-15765	143	7	extended	extend	VERB
fcis-15765	143	8	this	this	DET
fcis-15765	143	9	method	method	NOUN
fcis-15765	143	10	to	to	PART
fcis-15765	143	11	handle	handle	VERB
fcis-15765	143	12	three	three	NUM
fcis-15765	143	13	related	related	ADJ
fcis-15765	143	14	tasks	task	NOUN
fcis-15765	143	15	,	,	PUNCT
fcis-15765	143	16	namely	namely	ADV
fcis-15765	143	17	depth	depth	NOUN
fcis-15765	143	18	,	,	PUNCT
fcis-15765	143	19	surface	surface	VERB
fcis-15765	143	20	normal	normal	ADJ
fcis-15765	143	21	and	and	CCONJ
fcis-15765	143	22	semantic	semantic	ADJ
fcis-15765	143	23	label	label	NOUN
fcis-15765	143	24	prediction	prediction	NOUN
fcis-15765	143	25	.	.	PUNCT
fcis-15765	144	1	rosa	rosa	PROPN
fcis-15765	144	2	et	et	PROPN
fcis-15765	144	3	al	al	PROPN
fcis-15765	144	4	.	.	PUNCT
fcis-15765	145	1	[	[	X
fcis-15765	145	2	34	34	NUM
fcis-15765	145	3	]	]	PUNCT
fcis-15765	145	4	proposed	propose	VERB
fcis-15765	145	5	a	a	DET
fcis-15765	145	6	supervised	supervise	VERB
fcis-15765	145	7	framework	framework	NOUN
fcis-15765	145	8	to	to	PART
fcis-15765	145	9	estimate	estimate	VERB
fcis-15765	145	10	a	a	DET
fcis-15765	145	11	continuous	continuous	ADJ
fcis-15765	145	12	depth	depth	NOUN
fcis-15765	145	13	map	map	NOUN
fcis-15765	145	14	of	of	ADP
fcis-15765	145	15	lidar	lidar	NOUN
fcis-15765	145	16	points	point	NOUN
fcis-15765	145	17	.	.	PUNCT
fcis-15765	146	1	the	the	DET
fcis-15765	146	2	framework	framework	NOUN
fcis-15765	146	3	leverages	leverage	VERB
fcis-15765	146	4	the	the	DET
fcis-15765	146	5	hilbert	hilbert	NOUN
fcis-15765	146	6	map	map	NOUN
fcis-15765	146	7	method	method	NOUN
fcis-15765	146	8	[	[	X
fcis-15765	146	9	35	35	NUM
fcis-15765	146	10	]	]	PUNCT
fcis-15765	146	11	to	to	PART
fcis-15765	146	12	generate	generate	VERB
fcis-15765	146	13	a	a	DET
fcis-15765	146	14	dense	dense	ADJ
fcis-15765	146	15	depth	depth	NOUN
fcis-15765	146	16	map	map	NOUN
fcis-15765	146	17	from	from	ADP
fcis-15765	146	18	sparse	sparse	ADJ
fcis-15765	146	19	points	point	NOUN
fcis-15765	146	20	projected	project	VERB
fcis-15765	146	21	by	by	ADP
fcis-15765	146	22	a	a	DET
fcis-15765	146	23	lidar	lidar	NOUN
fcis-15765	146	24	scanner	scanner	NOUN
fcis-15765	146	25	.	.	PUNCT
fcis-15765	147	1	furthermore	furthermore	ADV
fcis-15765	147	2	,	,	PUNCT
fcis-15765	147	3	the	the	DET
fcis-15765	147	4	proposed	propose	VERB
fcis-15765	147	5	framework	framework	NOUN
fcis-15765	147	6	utilizes	utilize	VERB
fcis-15765	147	7	the	the	DET
fcis-15765	147	8	fully	fully	ADV
fcis-15765	147	9	convolutional	convolutional	ADJ
fcis-15765	147	10	residual	residual	ADJ
fcis-15765	147	11	network	network	NOUN
fcis-15765	147	12	(	(	PUNCT
fcis-15765	147	13	fcrn	fcrn	PROPN
fcis-15765	147	14	)	)	PUNCT
fcis-15765	148	1	[	[	X
fcis-15765	148	2	11	11	NUM
fcis-15765	148	3	]	]	PUNCT
fcis-15765	148	4	proposed	propose	VERB
fcis-15765	148	5	by	by	ADP
fcis-15765	148	6	laina	laina	PROPN
fcis-15765	148	7	et	et	PROPN
fcis-15765	148	8	al	al	PROPN
fcis-15765	148	9	.	.	PROPN
fcis-15765	148	10	for	for	ADP
fcis-15765	148	11	depth	depth	NOUN
fcis-15765	148	12	estimation	estimation	NOUN
fcis-15765	148	13	.	.	PUNCT
fcis-15765	149	1	the	the	DET
fcis-15765	149	2	network	network	NOUN
fcis-15765	149	3	is	be	AUX
fcis-15765	149	4	trained	train	VERB
fcis-15765	149	5	on	on	ADP
fcis-15765	149	6	dense	dense	ADJ
fcis-15765	149	7	depth	depth	NOUN
fcis-15765	149	8	images	image	NOUN
fcis-15765	149	9	that	that	PRON
fcis-15765	149	10	are	be	AUX
fcis-15765	149	11	augmented	augment	VERB
fcis-15765	149	12	by	by	ADP
fcis-15765	149	13	flipping	flip	VERB
fcis-15765	149	14	and	and	CCONJ
fcis-15765	149	15	applying	apply	VERB
fcis-15765	149	16	color	color	NOUN
fcis-15765	149	17	distortions	distortion	NOUN
fcis-15765	149	18	.	.	PUNCT
fcis-15765	150	1	although	although	SCONJ
fcis-15765	150	2	the	the	DET
fcis-15765	150	3	performance	performance	NOUN
fcis-15765	150	4	of	of	ADP
fcis-15765	150	5	this	this	DET
fcis-15765	150	6	method	method	NOUN
fcis-15765	150	7	is	be	AUX
fcis-15765	150	8	comparable	comparable	ADJ
fcis-15765	150	9	to	to	ADP
fcis-15765	150	10	the	the	DET
fcis-15765	150	11	state	state	NOUN
fcis-15765	150	12	-	-	PUNCT
fcis-15765	150	13	of	of	ADP
fcis-15765	150	14	-	-	PUNCT
fcis-15765	150	15	the	the	DET
fcis-15765	150	16	-	-	PUNCT
fcis-15765	150	17	art	art	NOUN
fcis-15765	150	18	methods	method	NOUN
fcis-15765	150	19	,	,	PUNCT
fcis-15765	150	20	it	it	PRON
fcis-15765	150	21	can	can	AUX
fcis-15765	150	22	only	only	ADV
fcis-15765	150	23	generate	generate	VERB
fcis-15765	150	24	depth	depth	NOUN
fcis-15765	150	25	maps	map	NOUN
fcis-15765	150	26	with	with	ADP
fcis-15765	150	27	a	a	DET
fcis-15765	150	28	resolution	resolution	NOUN
fcis-15765	150	29	of	of	ADP
fcis-15765	150	30	128	128	NUM
fcis-15765	150	31	×	×	NOUN
fcis-15765	150	32	160	160	NUM
fcis-15765	150	33	pixels	pixel	NOUN
fcis-15765	150	34	.	.	PUNCT
fcis-15765	151	1	more	more	ADV
fcis-15765	151	2	importantly	importantly	ADV
fcis-15765	151	3	,	,	PUNCT
fcis-15765	151	4	the	the	DET
fcis-15765	151	5	network	network	NOUN
fcis-15765	151	6	is	be	AUX
fcis-15765	151	7	biased	bias	VERB
fcis-15765	151	8	by	by	ADP
fcis-15765	151	9	the	the	DET
fcis-15765	151	10	output	output	NOUN
fcis-15765	151	11	of	of	ADP
fcis-15765	151	12	the	the	DET
fcis-15765	151	13	hilbert	hilbert	NOUN
fcis-15765	151	14	map	map	NOUN
fcis-15765	151	15	densification	densification	NOUN
fcis-15765	151	16	process	process	NOUN
fcis-15765	151	17	,	,	PUNCT
fcis-15765	151	18	which	which	PRON
fcis-15765	151	19	does	do	AUX
fcis-15765	151	20	not	not	PART
fcis-15765	151	21	represent	represent	VERB
fcis-15765	151	22	the	the	DET
fcis-15765	151	23	true	true	ADJ
fcis-15765	151	24	depth	depth	NOUN
fcis-15765	151	25	information	information	NOUN
fcis-15765	151	26	of	of	ADP
fcis-15765	151	27	the	the	DET
fcis-15765	151	28	missing	miss	VERB
fcis-15765	151	29	regions	region	NOUN
fcis-15765	151	30	.	.	PUNCT
fcis-15765	152	1	li	li	PROPN
fcis-15765	152	2	et	et	PROPN
fcis-15765	152	3	al	al	PROPN
fcis-15765	152	4	.	.	PUNCT
fcis-15765	153	1	[	[	X
fcis-15765	153	2	36	36	NUM
fcis-15765	153	3	]	]	X
fcis-15765	153	4	propose	propose	VERB
fcis-15765	153	5	a	a	DET
fcis-15765	153	6	hierarchically	hierarchically	ADV
fcis-15765	153	7	fused	fuse	VERB
fcis-15765	153	8	dilated	dilated	ADJ
fcis-15765	153	9	cnn	cnn	NOUN
fcis-15765	153	10	to	to	PART
fcis-15765	153	11	learn	learn	VERB
fcis-15765	153	12	the	the	DET
fcis-15765	153	13	mapping	mapping	NOUN
fcis-15765	153	14	between	between	ADP
fcis-15765	153	15	input	input	NOUN
fcis-15765	153	16	rgb	rgb	PROPN
fcis-15765	153	17	images	image	NOUN
fcis-15765	153	18	and	and	CCONJ
fcis-15765	153	19	corresponding	corresponding	ADJ
fcis-15765	153	20	depth	depth	NOUN
fcis-15765	153	21	maps	map	NOUN
fcis-15765	153	22	.	.	PUNCT
fcis-15765	154	1	soft	soft	ADJ
fcis-15765	154	2	weighting	weighting	NOUN
fcis-15765	154	3	and	and	CCONJ
fcis-15765	154	4	inference	inference	NOUN
fcis-15765	154	5	are	be	AUX
fcis-15765	154	6	proposed	propose	VERB
fcis-15765	154	7	to	to	PART
fcis-15765	154	8	convert	convert	VERB
fcis-15765	154	9	discrete	discrete	ADJ
fcis-15765	154	10	depth	depth	NOUN
fcis-15765	154	11	scores	score	NOUN
fcis-15765	154	12	to	to	ADP
fcis-15765	154	13	continuous	continuous	ADJ
fcis-15765	154	14	depth	depth	NOUN
fcis-15765	154	15	values	value	NOUN
fcis-15765	154	16	.	.	PUNCT
fcis-15765	155	1	following	follow	VERB
fcis-15765	155	2	[	[	X
fcis-15765	155	3	11	11	NUM
fcis-15765	155	4	]	]	PUNCT
fcis-15765	155	5	,	,	PUNCT
fcis-15765	155	6	[	[	X
fcis-15765	155	7	35	35	NUM
fcis-15765	155	8	]	]	PUNCT
fcis-15765	155	9	,	,	PUNCT
fcis-15765	155	10	[	[	X
fcis-15765	155	11	36	36	NUM
fcis-15765	155	12	]	]	PUNCT
fcis-15765	155	13	,	,	PUNCT
fcis-15765	155	14	zou	zou	PROPN
fcis-15765	155	15	et	et	PROPN
fcis-15765	155	16	al	al	PROPN
fcis-15765	155	17	.	.	PUNCT
fcis-15765	156	1	[	[	X
fcis-15765	156	2	37	37	NUM
fcis-15765	156	3	]	]	PUNCT
fcis-15765	156	4	treat	treat	NOUN
fcis-15765	156	5	depth	depth	NOUN
fcis-15765	156	6	estimation	estimation	NOUN
fcis-15765	156	7	as	as	ADP
fcis-15765	156	8	a	a	DET
fcis-15765	156	9	classification	classification	NOUN
fcis-15765	156	10	problem	problem	NOUN
fcis-15765	156	11	but	but	CCONJ
fcis-15765	156	12	consider	consider	VERB
fcis-15765	156	13	probability	probability	NOUN
fcis-15765	156	14	distributions	distribution	NOUN
fcis-15765	156	15	in	in	ADP
fcis-15765	156	16	the	the	DET
fcis-15765	156	17	training	training	NOUN
fcis-15765	156	18	step	step	NOUN
fcis-15765	156	19	.	.	PUNCT
fcis-15765	157	1	their	their	PRON
fcis-15765	157	2	main	main	ADJ
fcis-15765	157	3	contribution	contribution	NOUN
fcis-15765	157	4	is	be	AUX
fcis-15765	157	5	a	a	DET
fcis-15765	157	6	novel	novel	ADJ
fcis-15765	157	7	mean	mean	ADJ
fcis-15765	157	8	-	-	PUNCT
fcis-15765	157	9	variance	variance	NOUN
fcis-15765	157	10	loss	loss	NOUN
fcis-15765	157	11	,	,	PUNCT
fcis-15765	157	12	which	which	PRON
fcis-15765	157	13	consists	consist	VERB
fcis-15765	157	14	of	of	ADP
fcis-15765	157	15	a	a	DET
fcis-15765	157	16	mean	mean	ADJ
fcis-15765	157	17	loss	loss	NOUN
fcis-15765	157	18	and	and	CCONJ
fcis-15765	157	19	a	a	DET
fcis-15765	157	20	variance	variance	NOUN
fcis-15765	157	21	loss	loss	NOUN
fcis-15765	157	22	.	.	PUNCT
fcis-15765	158	1	the	the	DET
fcis-15765	158	2	averaging	averaging	NOUN
fcis-15765	158	3	loss	loss	NOUN
fcis-15765	158	4	is	be	AUX
fcis-15765	158	5	used	use	VERB
fcis-15765	158	6	to	to	PART
fcis-15765	158	7	penalize	penalize	VERB
fcis-15765	158	8	the	the	DET
fcis-15765	158	9	error	error	NOUN
fcis-15765	158	10	between	between	ADP
fcis-15765	158	11	the	the	DET
fcis-15765	158	12	mean	mean	NOUN
fcis-15765	158	13	of	of	ADP
fcis-15765	158	14	the	the	DET
fcis-15765	158	15	estimated	estimate	VERB
fcis-15765	158	16	depth	depth	NOUN
fcis-15765	158	17	distribution	distribution	NOUN
fcis-15765	158	18	and	and	CCONJ
fcis-15765	158	19	the	the	DET
fcis-15765	158	20	true	true	ADJ
fcis-15765	158	21	value	value	NOUN
fcis-15765	158	22	.	.	PUNCT
fcis-15765	159	1	at	at	ADP
fcis-15765	159	2	the	the	DET
fcis-15765	159	3	same	same	ADJ
fcis-15765	159	4	time	time	NOUN
fcis-15765	159	5	,	,	PUNCT
fcis-15765	159	6	the	the	DET
fcis-15765	159	7	variance	variance	NOUN
fcis-15765	159	8	loss	loss	NOUN
fcis-15765	159	9	is	be	AUX
fcis-15765	159	10	complementary	complementary	ADJ
fcis-15765	159	11	to	to	ADP
fcis-15765	159	12	the	the	DET
fcis-15765	159	13	mean	mean	ADJ
fcis-15765	159	14	loss	loss	NOUN
fcis-15765	159	15	to	to	PART
fcis-15765	159	16	make	make	VERB
fcis-15765	159	17	the	the	DET
fcis-15765	159	18	distribution	distribution	NOUN
fcis-15765	159	19	sharper	sharp	ADJ
fcis-15765	159	20	.	.	PUNCT
fcis-15765	160	1	the	the	DET
fcis-15765	160	2	mean	mean	ADJ
fcis-15765	160	3	-	-	PUNCT
fcis-15765	160	4	variance	variance	NOUN
fcis-15765	160	5	loss	loss	NOUN
fcis-15765	160	6	is	be	AUX
fcis-15765	160	7	combined	combine	VERB
fcis-15765	160	8	with	with	ADP
fcis-15765	160	9	a	a	DET
fcis-15765	160	10	softmax	softmax	NOUN
fcis-15765	160	11	loss	loss	NOUN
fcis-15765	160	12	to	to	PART
fcis-15765	160	13	supervise	supervise	VERB
fcis-15765	160	14	the	the	DET
fcis-15765	160	15	training	training	NOUN
fcis-15765	160	16	of	of	ADP
fcis-15765	160	17	the	the	DET
fcis-15765	160	18	depth	depth	NOUN
fcis-15765	160	19	estimation	estimation	NOUN
fcis-15765	160	20	network	network	NOUN
fcis-15765	160	21	.	.	PUNCT
fcis-15765	161	1	ladicky	ladicky	PROPN
fcis-15765	161	2	et	et	PROPN
fcis-15765	161	3	al	al	PROPN
fcis-15765	161	4	.	.	PUNCT
fcis-15765	162	1	[	[	X
fcis-15765	162	2	39	39	NUM
fcis-15765	162	3	]	]	PUNCT
fcis-15765	162	4	jointly	jointly	ADV
fcis-15765	162	5	predict	predict	VERB
fcis-15765	162	6	depth	depth	NOUN
fcis-15765	162	7	maps	map	NOUN
fcis-15765	162	8	and	and	CCONJ
fcis-15765	162	9	semantic	semantic	ADJ
fcis-15765	162	10	segmentation	segmentation	NOUN
fcis-15765	162	11	labels	label	VERB
fcis-15765	162	12	through	through	ADP
fcis-15765	162	13	a	a	DET
fcis-15765	162	14	pixel	pixel	ADJ
fcis-15765	162	15	-	-	PUNCT
fcis-15765	162	16	level	level	NOUN
fcis-15765	162	17	classifier	classifier	NOUN
fcis-15765	162	18	trained	train	VERB
fcis-15765	162	19	by	by	ADP
fcis-15765	162	20	ground	ground	NOUN
fcis-15765	162	21	truth	truth	NOUN
fcis-15765	162	22	depth	depth	NOUN
fcis-15765	162	23	and	and	CCONJ
fcis-15765	162	24	semantic	semantic	ADJ
fcis-15765	162	25	information	information	NOUN
fcis-15765	162	26	,	,	PUNCT
fcis-15765	162	27	while	while	SCONJ
fcis-15765	162	28	liu	liu	PROPN
fcis-15765	162	29	et	et	PROPN
fcis-15765	162	30	al	al	PROPN
fcis-15765	162	31	.	.	PUNCT
fcis-15765	163	1	[	[	X
fcis-15765	163	2	40	40	NUM
fcis-15765	163	3	]	]	PUNCT
fcis-15765	163	4	redefine	redefine	VERB
fcis-15765	163	5	depth	depth	NOUN
fcis-15765	163	6	prediction	prediction	NOUN
fcis-15765	163	7	as	as	ADP
fcis-15765	163	8	a	a	DET
fcis-15765	163	9	discrete	discrete	ADJ
fcis-15765	163	10	optimization	optimization	NOUN
fcis-15765	163	11	problems	problem	NOUN
fcis-15765	163	12	for	for	ADP
fcis-15765	163	13	continuous	continuous	ADJ
fcis-15765	163	14	graphical	graphical	ADJ
fcis-15765	163	15	models	model	NOUN
fcis-15765	163	16	.	.	PUNCT
fcis-15765	164	1	from	from	ADP
fcis-15765	164	2	2020	2020	NUM
fcis-15765	164	3	to	to	ADP
fcis-15765	164	4	2021	2021	NUM
fcis-15765	164	5	,	,	PUNCT
fcis-15765	164	6	transformer	transformer	NOUN
fcis-15765	164	7	[	[	X
fcis-15765	164	8	41	41	NUM
fcis-15765	164	9	]	]	PUNCT
fcis-15765	164	10	in	in	ADP
fcis-15765	164	11	the	the	DET
fcis-15765	164	12	field	field	NOUN
fcis-15765	164	13	of	of	ADP
fcis-15765	164	14	natural	natural	ADJ
fcis-15765	164	15	language	language	NOUN
fcis-15765	164	16	processing	processing	NOUN
fcis-15765	164	17	will	will	AUX
fcis-15765	164	18	become	become	VERB
fcis-15765	164	19	popular	popular	ADJ
fcis-15765	164	20	.	.	PUNCT
fcis-15765	165	1	researchers	researcher	NOUN
fcis-15765	165	2	have	have	AUX
fcis-15765	165	3	introduced	introduce	VERB
fcis-15765	165	4	this	this	DET
fcis-15765	165	5	attention	attention	NOUN
fcis-15765	165	6	mechanism	mechanism	NOUN
fcis-15765	165	7	-	-	PUNCT
fcis-15765	165	8	based	base	VERB
fcis-15765	165	9	network	network	NOUN
fcis-15765	165	10	structure	structure	NOUN
fcis-15765	165	11	into	into	ADP
fcis-15765	165	12	the	the	DET
fcis-15765	165	13	field	field	NOUN
fcis-15765	165	14	of	of	ADP
fcis-15765	165	15	computer	computer	NOUN
fcis-15765	165	16	vision	vision	NOUN
fcis-15765	165	17	and	and	CCONJ
fcis-15765	165	18	achieved	achieve	VERB
fcis-15765	165	19	excellent	excellent	ADJ
fcis-15765	165	20	results	result	NOUN
fcis-15765	165	21	.	.	PUNCT
fcis-15765	166	1	in	in	ADP
fcis-15765	166	2	the	the	DET
fcis-15765	166	3	field	field	NOUN
fcis-15765	166	4	of	of	ADP
fcis-15765	166	5	depth	depth	NOUN
fcis-15765	166	6	estimation	estimation	NOUN
fcis-15765	166	7	,	,	PUNCT
fcis-15765	166	8	ranftl	ranftl	NOUN
fcis-15765	166	9	et	et	NOUN
fcis-15765	166	10	al	al	PROPN
fcis-15765	166	11	.	.	PUNCT
fcis-15765	167	1	[	[	X
fcis-15765	167	2	42	42	NUM
fcis-15765	167	3	]	]	PUNCT
fcis-15765	167	4	first	first	ADV
fcis-15765	167	5	tried	try	VERB
fcis-15765	167	6	to	to	PART
fcis-15765	167	7	use	use	VERB
fcis-15765	167	8	transformer	transformer	NOUN
fcis-15765	167	9	to	to	PART
fcis-15765	167	10	replace	replace	VERB
fcis-15765	167	11	convolutional	convolutional	ADJ
fcis-15765	167	12	neural	neural	ADJ
fcis-15765	167	13	network	network	NOUN
fcis-15765	167	14	as	as	ADP
fcis-15765	167	15	a	a	DET
fcis-15765	167	16	feature	feature	NOUN
fcis-15765	167	17	extractor	extractor	NOUN
fcis-15765	167	18	,	,	PUNCT
fcis-15765	167	19	and	and	CCONJ
fcis-15765	167	20	applied	apply	VERB
fcis-15765	167	21	it	it	PRON
fcis-15765	167	22	to	to	ADP
fcis-15765	167	23	dense	dense	ADJ
fcis-15765	167	24	prediction	prediction	NOUN
fcis-15765	167	25	problems	problem	NOUN
fcis-15765	167	26	.	.	PUNCT
fcis-15765	168	1	by	by	ADP
fcis-15765	168	2	combining	combine	VERB
fcis-15765	168	3	global	global	ADJ
fcis-15765	168	4	and	and	CCONJ
fcis-15765	168	5	local	local	ADJ
fcis-15765	168	6	attention	attention	NOUN
fcis-15765	168	7	features	feature	NOUN
fcis-15765	168	8	,	,	PUNCT
fcis-15765	168	9	they	they	PRON
fcis-15765	168	10	achieved	achieve	VERB
fcis-15765	168	11	excellent	excellent	ADJ
fcis-15765	168	12	depth	depth	NOUN
fcis-15765	168	13	estimation	estimation	NOUN
fcis-15765	168	14	.	.	PUNCT
fcis-15765	169	1	result	result	VERB
fcis-15765	169	2	.	.	PUNCT
fcis-15765	170	1	as	as	ADP
fcis-15765	170	2	31	31	NUM
fcis-15765	170	3	the	the	DET
fcis-15765	170	4	first	first	ADJ
fcis-15765	170	5	article	article	NOUN
fcis-15765	170	6	to	to	PART
fcis-15765	170	7	introduce	introduce	VERB
fcis-15765	170	8	this	this	DET
fcis-15765	170	9	structure	structure	NOUN
fcis-15765	170	10	into	into	ADP
fcis-15765	170	11	depth	depth	NOUN
fcis-15765	170	12	estimation	estimation	NOUN
fcis-15765	170	13	,	,	PUNCT
fcis-15765	170	14	their	their	PRON
fcis-15765	170	15	results	result	NOUN
fcis-15765	170	16	further	far	ADV
fcis-15765	170	17	triggered	trigger	VERB
fcis-15765	170	18	scholars	scholar	NOUN
fcis-15765	170	19	'	'	PART
fcis-15765	170	20	thinking	thinking	NOUN
fcis-15765	170	21	on	on	ADP
fcis-15765	170	22	the	the	DET
fcis-15765	170	23	transformer	transformer	NOUN
fcis-15765	170	24	structure	structure	NOUN
fcis-15765	170	25	.	.	PUNCT
fcis-15765	171	1	how	how	SCONJ
fcis-15765	171	2	to	to	PART
fcis-15765	171	3	design	design	VERB
fcis-15765	171	4	a	a	DET
fcis-15765	171	5	transformer	transformer	NOUN
fcis-15765	171	6	structure	structure	NOUN
fcis-15765	171	7	that	that	PRON
fcis-15765	171	8	is	be	AUX
fcis-15765	171	9	more	more	ADV
fcis-15765	171	10	suitable	suitable	ADJ
fcis-15765	171	11	for	for	ADP
fcis-15765	171	12	depth	depth	NOUN
fcis-15765	171	13	estimation	estimation	NOUN
fcis-15765	171	14	is	be	AUX
fcis-15765	171	15	a	a	DET
fcis-15765	171	16	place	place	NOUN
fcis-15765	171	17	that	that	PRON
fcis-15765	171	18	needs	need	VERB
fcis-15765	171	19	further	further	ADJ
fcis-15765	171	20	exploration	exploration	NOUN
fcis-15765	171	21	.	.	PUNCT
fcis-15765	172	1	starting	start	VERB
fcis-15765	172	2	from	from	ADP
fcis-15765	172	3	different	different	ADJ
fcis-15765	172	4	perspectives	perspective	NOUN
fcis-15765	172	5	,	,	PUNCT
fcis-15765	172	6	bhat	bhat	PROPN
fcis-15765	172	7	et	et	PROPN
fcis-15765	172	8	al	al	PROPN
fcis-15765	172	9	.	.	PUNCT
fcis-15765	173	1	[	[	X
fcis-15765	173	2	43	43	NUM
fcis-15765	173	3	]	]	PUNCT
fcis-15765	173	4	introduced	introduce	VERB
fcis-15765	173	5	transformer	transformer	NOUN
fcis-15765	173	6	as	as	ADP
fcis-15765	173	7	a	a	DET
fcis-15765	173	8	spatial	spatial	ADJ
fcis-15765	173	9	attention	attention	NOUN
fcis-15765	173	10	module	module	NOUN
fcis-15765	173	11	into	into	ADP
fcis-15765	173	12	their	their	PRON
fcis-15765	173	13	framework	framework	NOUN
fcis-15765	173	14	.	.	PUNCT
fcis-15765	174	1	their	their	PRON
fcis-15765	174	2	method	method	NOUN
fcis-15765	174	3	further	far	ADV
fcis-15765	174	4	explored	explore	VERB
fcis-15765	174	5	the	the	DET
fcis-15765	174	6	potential	potential	NOUN
fcis-15765	174	7	of	of	ADP
fcis-15765	174	8	transformer	transformer	NOUN
fcis-15765	174	9	,	,	PUNCT
fcis-15765	174	10	a	a	DET
fcis-15765	174	11	network	network	NOUN
fcis-15765	174	12	structure	structure	NOUN
fcis-15765	174	13	,	,	PUNCT
fcis-15765	174	14	and	and	CCONJ
fcis-15765	174	15	proved	prove	VERB
fcis-15765	174	16	that	that	SCONJ
fcis-15765	174	17	the	the	DET
fcis-15765	174	18	spatial	spatial	ADJ
fcis-15765	174	19	attention	attention	NOUN
fcis-15765	174	20	mechanism	mechanism	NOUN
fcis-15765	174	21	is	be	AUX
fcis-15765	174	22	beneficial	beneficial	ADJ
fcis-15765	174	23	to	to	ADP
fcis-15765	174	24	monocular	monocular	ADJ
fcis-15765	174	25	depth	depth	NOUN
fcis-15765	174	26	estimation	estimation	NOUN
fcis-15765	174	27	plays	play	VERB
fcis-15765	174	28	an	an	DET
fcis-15765	174	29	important	important	ADJ
fcis-15765	174	30	role	role	NOUN
fcis-15765	174	31	.	.	PUNCT
fcis-15765	175	1	in	in	ADP
fcis-15765	175	2	[	[	X
fcis-15765	175	3	43	43	NUM
fcis-15765	175	4	]	]	PUNCT
fcis-15765	175	5	,	,	PUNCT
fcis-15765	175	6	jung	jung	PROPN
fcis-15765	175	7	et	et	PROPN
fcis-15765	175	8	al	al	PROPN
fcis-15765	175	9	.	.	PROPN
fcis-15765	175	10	introduced	introduce	VERB
fcis-15765	175	11	adversarial	adversarial	ADJ
fcis-15765	175	12	learning	learning	NOUN
fcis-15765	175	13	to	to	ADP
fcis-15765	175	14	the	the	DET
fcis-15765	175	15	task	task	NOUN
fcis-15765	175	16	of	of	ADP
fcis-15765	175	17	monocular	monocular	ADJ
fcis-15765	175	18	depth	depth	NOUN
fcis-15765	175	19	estimation	estimation	NOUN
fcis-15765	175	20	.	.	PUNCT
fcis-15765	176	1	the	the	DET
fcis-15765	176	2	generator	generator	NOUN
fcis-15765	176	3	consists	consist	VERB
fcis-15765	176	4	of	of	ADP
fcis-15765	176	5	a	a	DET
fcis-15765	176	6	global	global	ADJ
fcis-15765	176	7	network	network	NOUN
fcis-15765	176	8	and	and	CCONJ
fcis-15765	176	9	a	a	DET
fcis-15765	176	10	refinement	refinement	NOUN
fcis-15765	176	11	network	network	NOUN
fcis-15765	176	12	,	,	PUNCT
fcis-15765	176	13	which	which	PRON
fcis-15765	176	14	aim	aim	VERB
fcis-15765	176	15	to	to	PART
fcis-15765	176	16	estimate	estimate	VERB
fcis-15765	176	17	global	global	ADJ
fcis-15765	176	18	and	and	CCONJ
fcis-15765	176	19	local	local	ADJ
fcis-15765	176	20	3d	3d	PROPN
fcis-15765	176	21	structures	structure	NOUN
fcis-15765	176	22	from	from	ADP
fcis-15765	176	23	a	a	DET
fcis-15765	176	24	single	single	ADJ
fcis-15765	176	25	image	image	NOUN
fcis-15765	176	26	.	.	PUNCT
fcis-15765	177	1	then	then	ADV
fcis-15765	177	2	,	,	PUNCT
fcis-15765	177	3	a	a	DET
fcis-15765	177	4	discriminator	discriminator	NOUN
fcis-15765	177	5	is	be	AUX
fcis-15765	177	6	used	use	VERB
fcis-15765	177	7	to	to	PART
fcis-15765	177	8	distinguish	distinguish	VERB
fcis-15765	177	9	the	the	DET
fcis-15765	177	10	predicted	predict	VERB
fcis-15765	177	11	and	and	CCONJ
fcis-15765	177	12	ground	ground	NOUN
fcis-15765	177	13	-	-	PUNCT
fcis-15765	177	14	truth	truth	NOUN
fcis-15765	177	15	depth	depth	NOUN
fcis-15765	177	16	maps	map	NOUN
fcis-15765	177	17	,	,	PUNCT
fcis-15765	177	18	a	a	DET
fcis-15765	177	19	form	form	NOUN
fcis-15765	177	20	that	that	PRON
fcis-15765	177	21	is	be	AUX
fcis-15765	177	22	often	often	ADV
fcis-15765	177	23	used	use	VERB
fcis-15765	177	24	in	in	ADP
fcis-15765	177	25	supervised	supervised	ADJ
fcis-15765	177	26	methods	method	NOUN
fcis-15765	177	27	.	.	PUNCT
fcis-15765	178	1	the	the	DET
fcis-15765	178	2	confrontation	confrontation	NOUN
fcis-15765	178	3	between	between	ADP
fcis-15765	178	4	generator	generator	NOUN
fcis-15765	178	5	and	and	CCONJ
fcis-15765	178	6	discriminator	discriminator	NOUN
fcis-15765	178	7	facilitates	facilitate	VERB
fcis-15765	178	8	the	the	DET
fcis-15765	178	9	training	training	NOUN
fcis-15765	178	10	of	of	ADP
fcis-15765	178	11	min	min	ADJ
fcis-15765	178	12	-	-	ADJ
fcis-15765	178	13	max	max	PROPN
fcis-15765	178	14	problem	problem	NOUN
fcis-15765	178	15	based	base	VERB
fcis-15765	178	16	frameworks	framework	NOUN
fcis-15765	178	17	.	.	PUNCT
fcis-15765	179	1	5	5	X
fcis-15765	179	2	.	.	X
fcis-15765	179	3	unsupervised	unsupervised	ADJ
fcis-15765	179	4	monocular	monocular	ADJ
fcis-15765	179	5	depth	depth	NOUN
fcis-15765	179	6	estimation	estimation	NOUN
fcis-15765	179	7	during	during	ADP
fcis-15765	179	8	the	the	DET
fcis-15765	179	9	training	training	NOUN
fcis-15765	179	10	of	of	ADP
fcis-15765	179	11	unsupervised	unsupervised	ADJ
fcis-15765	179	12	methods	method	NOUN
fcis-15765	179	13	,	,	PUNCT
fcis-15765	179	14	instead	instead	ADV
fcis-15765	179	15	of	of	ADP
fcis-15765	179	16	using	use	VERB
fcis-15765	179	17	expensively	expensively	ADV
fcis-15765	179	18	acquired	acquire	VERB
fcis-15765	179	19	ground	ground	NOUN
fcis-15765	179	20	truth	truth	NOUN
fcis-15765	179	21	,	,	PUNCT
fcis-15765	179	22	geometric	geometric	ADJ
fcis-15765	179	23	constraints	constraint	NOUN
fcis-15765	179	24	between	between	ADP
fcis-15765	179	25	frames	frame	NOUN
fcis-15765	179	26	are	be	AUX
fcis-15765	179	27	considered	consider	VERB
fcis-15765	179	28	as	as	ADP
fcis-15765	179	29	supervisory	supervisory	ADJ
fcis-15765	179	30	signals	signal	NOUN
fcis-15765	179	31	.	.	PUNCT
fcis-15765	180	1	another	another	DET
fcis-15765	180	2	trend	trend	NOUN
fcis-15765	180	3	in	in	ADP
fcis-15765	180	4	monocular	monocular	ADJ
fcis-15765	180	5	depth	depth	NOUN
fcis-15765	180	6	estimation	estimation	NOUN
fcis-15765	180	7	is	be	AUX
fcis-15765	180	8	unsupervised	unsupervised	ADJ
fcis-15765	180	9	learning	learning	NOUN
fcis-15765	180	10	,	,	PUNCT
fcis-15765	180	11	where	where	SCONJ
fcis-15765	180	12	image	image	NOUN
fcis-15765	180	13	reconstructions	reconstruction	NOUN
fcis-15765	180	14	of	of	ADP
fcis-15765	180	15	stereo	stereo	ADJ
fcis-15765	180	16	image	image	NOUN
fcis-15765	180	17	pairs	pair	NOUN
fcis-15765	180	18	or	or	CCONJ
fcis-15765	180	19	monocular	monocular	ADJ
fcis-15765	180	20	video	video	NOUN
fcis-15765	180	21	sequences	sequence	NOUN
fcis-15765	180	22	are	be	AUX
fcis-15765	180	23	treated	treat	VERB
fcis-15765	180	24	as	as	ADP
fcis-15765	180	25	supervisory	supervisory	ADJ
fcis-15765	180	26	signals	signal	NOUN
fcis-15765	180	27	,	,	PUNCT
fcis-15765	180	28	and	and	CCONJ
fcis-15765	180	29	depth	depth	NOUN
fcis-15765	180	30	maps	map	NOUN
fcis-15765	180	31	are	be	AUX
fcis-15765	180	32	intermediate	intermediate	ADJ
fcis-15765	180	33	products	product	NOUN
fcis-15765	180	34	.	.	PUNCT
fcis-15765	181	1	xie	xie	PROPN
fcis-15765	181	2	et	et	PROPN
fcis-15765	181	3	al	al	PROPN
fcis-15765	181	4	.	.	PUNCT
fcis-15765	182	1	[	[	X
fcis-15765	182	2	44	44	NUM
fcis-15765	182	3	]	]	PUNCT
fcis-15765	182	4	used	use	VERB
fcis-15765	182	5	synchronized	synchronize	VERB
fcis-15765	182	6	stereo	stereo	NOUN
fcis-15765	182	7	images	image	NOUN
fcis-15765	182	8	in	in	ADP
fcis-15765	182	9	the	the	DET
fcis-15765	182	10	training	training	NOUN
fcis-15765	182	11	phase	phase	NOUN
fcis-15765	182	12	to	to	PART
fcis-15765	182	13	obtain	obtain	VERB
fcis-15765	182	14	discretized	discretized	ADJ
fcis-15765	182	15	depth	depth	NOUN
fcis-15765	182	16	from	from	ADP
fcis-15765	182	17	soft	soft	ADJ
fcis-15765	182	18	disparity	disparity	NOUN
fcis-15765	182	19	maps	map	NOUN
fcis-15765	182	20	by	by	ADP
fcis-15765	182	21	minimizing	minimize	VERB
fcis-15765	182	22	the	the	DET
fcis-15765	182	23	reconstruction	reconstruction	NOUN
fcis-15765	182	24	error	error	NOUN
fcis-15765	182	25	between	between	ADP
fcis-15765	182	26	the	the	DET
fcis-15765	182	27	right	right	ADJ
fcis-15765	182	28	view	view	NOUN
fcis-15765	182	29	and	and	CCONJ
fcis-15765	182	30	the	the	DET
fcis-15765	182	31	right	right	ADJ
fcis-15765	182	32	view	view	NOUN
fcis-15765	182	33	generated	generate	VERB
fcis-15765	182	34	from	from	ADP
fcis-15765	182	35	the	the	DET
fcis-15765	182	36	left	left	ADJ
fcis-15765	182	37	view	view	NOUN
fcis-15765	182	38	.	.	PUNCT
fcis-15765	183	1	garg	garg	PROPN
fcis-15765	183	2	et	et	PROPN
fcis-15765	183	3	al	al	PROPN
fcis-15765	183	4	.	.	PUNCT
fcis-15765	184	1	[	[	X
fcis-15765	184	2	13	13	NUM
fcis-15765	184	3	]	]	PUNCT
fcis-15765	184	4	extended	extend	VERB
fcis-15765	184	5	this	this	DET
fcis-15765	184	6	approach	approach	NOUN
fcis-15765	184	7	to	to	AUX
fcis-15765	184	8	output	output	VERB
fcis-15765	184	9	continuous	continuous	ADJ
fcis-15765	184	10	depth	depth	NOUN
fcis-15765	184	11	values	value	NOUN
fcis-15765	184	12	,	,	PUNCT
fcis-15765	184	13	but	but	CCONJ
fcis-15765	184	14	their	their	PRON
fcis-15765	184	15	image	image	NOUN
fcis-15765	184	16	formation	formation	NOUN
fcis-15765	184	17	model	model	NOUN
fcis-15765	184	18	is	be	AUX
fcis-15765	184	19	not	not	PART
fcis-15765	184	20	fully	fully	ADV
fcis-15765	184	21	differentiable	differentiable	ADJ
fcis-15765	184	22	and	and	CCONJ
fcis-15765	184	23	thus	thus	ADV
fcis-15765	184	24	difficult	difficult	ADJ
fcis-15765	184	25	to	to	PART
fcis-15765	184	26	optimize	optimize	VERB
fcis-15765	184	27	.	.	PUNCT
fcis-15765	185	1	godard	godard	PROPN
fcis-15765	185	2	et	et	PROPN
fcis-15765	185	3	al	al	PROPN
fcis-15765	186	1	[	[	X
fcis-15765	186	2	32	32	NUM
fcis-15765	186	3	]	]	PUNCT
fcis-15765	186	4	employ	employ	NOUN
fcis-15765	186	5	bilinear	bilinear	NOUN
fcis-15765	186	6	samplers	sampler	NOUN
fcis-15765	186	7	in	in	ADP
fcis-15765	186	8	a	a	DET
fcis-15765	186	9	spatial	spatial	ADJ
fcis-15765	186	10	transformation	transformation	NOUN
fcis-15765	186	11	network	network	NOUN
fcis-15765	186	12	(	(	PUNCT
fcis-15765	186	13	stn	stn	PROPN
fcis-15765	186	14	)	)	PUNCT
fcis-15765	186	15	(	(	PUNCT
fcis-15765	186	16	jaderberg	jaderberg	PROPN
fcis-15765	186	17	et	et	PROPN
fcis-15765	186	18	al	al	PROPN
fcis-15765	187	1	[	[	X
fcis-15765	187	2	45	45	NUM
fcis-15765	187	3	]	]	PUNCT
fcis-15765	187	4	)	)	PUNCT
fcis-15765	187	5	for	for	ADP
fcis-15765	187	6	fully	fully	ADV
fcis-15765	187	7	differentiable	differentiable	ADJ
fcis-15765	187	8	operations	operation	NOUN
fcis-15765	187	9	,	,	PUNCT
fcis-15765	187	10	and	and	CCONJ
fcis-15765	187	11	first	first	ADV
fcis-15765	187	12	introduce	introduce	VERB
fcis-15765	187	13	the	the	DET
fcis-15765	187	14	left	left	ADJ
fcis-15765	187	15	-	-	PUNCT
fcis-15765	187	16	right	right	NOUN
fcis-15765	187	17	consistency	consistency	NOUN
fcis-15765	187	18	of	of	ADP
fcis-15765	187	19	stereo	stereo	NOUN
fcis-15765	187	20	images	image	NOUN
fcis-15765	187	21	to	to	PART
fcis-15765	187	22	train	train	VERB
fcis-15765	187	23	a	a	DET
fcis-15765	187	24	depth	depth	NOUN
fcis-15765	187	25	estimation	estimation	NOUN
fcis-15765	187	26	network	network	NOUN
fcis-15765	187	27	.	.	PUNCT
fcis-15765	188	1	tosi	tosi	NOUN
fcis-15765	188	2	et	et	PROPN
fcis-15765	188	3	al	al	PROPN
fcis-15765	188	4	.	.	PUNCT
fcis-15765	189	1	[	[	X
fcis-15765	189	2	46	46	NUM
fcis-15765	189	3	]	]	PUNCT
fcis-15765	189	4	designed	design	VERB
fcis-15765	189	5	a	a	DET
fcis-15765	189	6	new	new	ADJ
fcis-15765	189	7	depth	depth	NOUN
fcis-15765	189	8	architecture	architecture	NOUN
fcis-15765	189	9	for	for	ADP
fcis-15765	189	10	monocular	monocular	ADJ
fcis-15765	189	11	depth	depth	NOUN
fcis-15765	189	12	estimation	estimation	NOUN
fcis-15765	189	13	by	by	ADP
fcis-15765	189	14	synthesizing	synthesize	VERB
fcis-15765	189	15	features	feature	NOUN
fcis-15765	189	16	from	from	ADP
fcis-15765	189	17	different	different	ADJ
fcis-15765	189	18	viewpoints	viewpoint	NOUN
fcis-15765	189	19	as	as	ADP
fcis-15765	189	20	input	input	NOUN
fcis-15765	189	21	to	to	ADP
fcis-15765	189	22	a	a	DET
fcis-15765	189	23	disparity	disparity	NOUN
fcis-15765	189	24	refinement	refinement	NOUN
fcis-15765	189	25	model	model	NOUN
fcis-15765	189	26	,	,	PUNCT
fcis-15765	189	27	and	and	CCONJ
fcis-15765	189	28	proposed	propose	VERB
fcis-15765	189	29	proxy	proxy	NOUN
fcis-15765	189	30	ground	ground	NOUN
fcis-15765	189	31	-	-	PUNCT
fcis-15765	189	32	truth	truth	NOUN
fcis-15765	189	33	annotations	annotation	NOUN
fcis-15765	189	34	via	via	ADP
fcis-15765	189	35	stereo	stereo	ADJ
fcis-15765	189	36	traditional	traditional	ADJ
fcis-15765	189	37	knowledge	knowledge	NOUN
fcis-15765	189	38	,	,	PUNCT
fcis-15765	189	39	i.e.	i.e.	X
fcis-15765	189	40	semiglobal	semiglobal	ADJ
fcis-15765	189	41	matching	matching	NOUN
fcis-15765	189	42	(	(	PUNCT
fcis-15765	189	43	sgm	sgm	PROPN
fcis-15765	189	44	)	)	PUNCT
fcis-15765	189	45	.	.	PUNCT
fcis-15765	190	1	wong	wong	PROPN
fcis-15765	190	2	and	and	CCONJ
fcis-15765	190	3	soatto	soatto	NOUN
fcis-15765	190	4	[	[	X
fcis-15765	190	5	47	47	NUM
fcis-15765	190	6	]	]	PUNCT
fcis-15765	190	7	introduced	introduce	VERB
fcis-15765	190	8	a	a	DET
fcis-15765	190	9	bilateral	bilateral	ADJ
fcis-15765	190	10	cycle	cycle	NOUN
fcis-15765	190	11	consistency	consistency	NOUN
fcis-15765	190	12	constraint	constraint	NOUN
fcis-15765	190	13	to	to	PART
fcis-15765	190	14	enforce	enforce	VERB
fcis-15765	190	15	consistency	consistency	NOUN
fcis-15765	190	16	between	between	ADP
fcis-15765	190	17	left	left	ADJ
fcis-15765	190	18	and	and	CCONJ
fcis-15765	190	19	right	right	ADJ
fcis-15765	190	20	disparities	disparity	NOUN
fcis-15765	190	21	and	and	CCONJ
fcis-15765	190	22	eliminate	eliminate	VERB
fcis-15765	190	23	stereo	stereo	NOUN
fcis-15765	190	24	de	de	NOUN
fcis-15765	190	25	-	-	NOUN
fcis-15765	190	26	occlusion	occlusion	NOUN
fcis-15765	190	27	.	.	PUNCT
fcis-15765	191	1	furthermore	furthermore	ADV
fcis-15765	191	2	,	,	PUNCT
fcis-15765	191	3	they	they	PRON
fcis-15765	191	4	propose	propose	VERB
fcis-15765	191	5	a	a	DET
fcis-15765	191	6	model	model	NOUN
fcis-15765	191	7	-	-	PUNCT
fcis-15765	191	8	driven	drive	VERB
fcis-15765	191	9	adaptive	adaptive	ADJ
fcis-15765	191	10	weighting	weighting	NOUN
fcis-15765	191	11	scheme	scheme	NOUN
fcis-15765	191	12	to	to	ADP
fcis-15765	191	13	better	well	ADJ
fcis-15765	191	14	balance	balance	NOUN
fcis-15765	191	15	data	datum	NOUN
fcis-15765	191	16	fidelity	fidelity	NOUN
fcis-15765	191	17	and	and	CCONJ
fcis-15765	191	18	regularization	regularization	NOUN
fcis-15765	191	19	.	.	PUNCT
fcis-15765	192	1	on	on	ADP
fcis-15765	192	2	the	the	DET
fcis-15765	192	3	other	other	ADJ
fcis-15765	192	4	hand	hand	NOUN
fcis-15765	192	5	,	,	PUNCT
fcis-15765	192	6	monocular	monocular	ADJ
fcis-15765	192	7	video	video	NOUN
fcis-15765	192	8	sequences	sequence	NOUN
fcis-15765	192	9	are	be	AUX
fcis-15765	192	10	used	use	VERB
fcis-15765	192	11	in	in	ADP
fcis-15765	192	12	the	the	DET
fcis-15765	192	13	training	training	NOUN
fcis-15765	192	14	phase	phase	NOUN
fcis-15765	192	15	.	.	PUNCT
fcis-15765	193	1	for	for	ADP
fcis-15765	193	2	training	train	VERB
fcis-15765	193	3	deep	deep	ADJ
fcis-15765	193	4	prediction	prediction	NOUN
fcis-15765	193	5	networks	network	NOUN
fcis-15765	193	6	,	,	PUNCT
fcis-15765	193	7	consecutive	consecutive	ADJ
fcis-15765	193	8	frames	frame	NOUN
fcis-15765	193	9	in	in	ADP
fcis-15765	193	10	videos	video	NOUN
fcis-15765	193	11	may	may	AUX
fcis-15765	193	12	have	have	VERB
fcis-15765	193	13	great	great	ADJ
fcis-15765	193	14	potential	potential	NOUN
fcis-15765	193	15	as	as	ADP
fcis-15765	193	16	a	a	DET
fcis-15765	193	17	supervised	supervised	ADJ
fcis-15765	193	18	application	application	NOUN
fcis-15765	193	19	.	.	PUNCT
fcis-15765	194	1	camera	camera	NOUN
fcis-15765	194	2	transformation	transformation	NOUN
fcis-15765	194	3	estimation	estimation	NOUN
fcis-15765	194	4	(	(	PUNCT
fcis-15765	194	5	pose	pose	NOUN
fcis-15765	194	6	estimation	estimation	NOUN
fcis-15765	194	7	)	)	PUNCT
fcis-15765	194	8	between	between	ADP
fcis-15765	194	9	consecutive	consecutive	ADJ
fcis-15765	194	10	frames	frame	NOUN
fcis-15765	194	11	is	be	AUX
fcis-15765	194	12	the	the	DET
fcis-15765	194	13	main	main	ADJ
fcis-15765	194	14	challenge	challenge	NOUN
fcis-15765	194	15	of	of	ADP
fcis-15765	194	16	this	this	DET
fcis-15765	194	17	process	process	NOUN
fcis-15765	194	18	,	,	PUNCT
fcis-15765	194	19	which	which	PRON
fcis-15765	194	20	leads	lead	VERB
fcis-15765	194	21	to	to	ADP
fcis-15765	194	22	additional	additional	ADJ
fcis-15765	194	23	complexity	complexity	NOUN
fcis-15765	194	24	for	for	ADP
fcis-15765	194	25	the	the	DET
fcis-15765	194	26	network	network	NOUN
fcis-15765	194	27	.	.	PUNCT
fcis-15765	195	1	as	as	SCONJ
fcis-15765	195	2	shown	show	VERB
fcis-15765	195	3	in	in	ADP
fcis-15765	195	4	figure	figure	NOUN
fcis-15765	195	5	2	2	NUM
fcis-15765	195	6	,	,	PUNCT
fcis-15765	195	7	zhou	zhou	PROPN
fcis-15765	195	8	et	et	PROPN
fcis-15765	195	9	al	al	PROPN
fcis-15765	195	10	.	.	PUNCT
fcis-15765	196	1	[	[	X
fcis-15765	196	2	14	14	NUM
fcis-15765	196	3	]	]	PUNCT
fcis-15765	196	4	developed	develop	VERB
fcis-15765	196	5	a	a	DET
fcis-15765	196	6	computer	computer	NOUN
fcis-15765	196	7	-	-	PUNCT
fcis-15765	196	8	based	base	VERB
fcis-15765	196	9	architecture	architecture	NOUN
fcis-15765	196	10	to	to	PART
fcis-15765	196	11	simultaneously	simultaneously	ADV
fcis-15765	196	12	estimate	estimate	VERB
fcis-15765	196	13	depth	depth	NOUN
fcis-15765	196	14	maps	map	NOUN
fcis-15765	196	15	and	and	CCONJ
fcis-15765	196	16	camera	camera	NOUN
fcis-15765	196	17	poses	pose	NOUN
fcis-15765	196	18	.	.	PUNCT
fcis-15765	197	1	as	as	ADP
fcis-15765	197	2	input	input	NOUN
fcis-15765	197	3	,	,	PUNCT
fcis-15765	197	4	three	three	NUM
fcis-15765	197	5	consecutive	consecutive	ADJ
fcis-15765	197	6	frames	frame	NOUN
fcis-15765	197	7	are	be	AUX
fcis-15765	197	8	fed	feed	VERB
fcis-15765	197	9	to	to	ADP
fcis-15765	197	10	the	the	DET
fcis-15765	197	11	network	network	NOUN
fcis-15765	197	12	.	.	PUNCT
fcis-15765	198	1	pose	pose	VERB
fcis-15765	198	2	cnn	cnn	PROPN
fcis-15765	198	3	and	and	CCONJ
fcis-15765	198	4	depth	depth	NOUN
fcis-15765	198	5	cnn	cnn	NOUN
fcis-15765	198	6	estimate	estimate	VERB
fcis-15765	198	7	relative	relative	ADJ
fcis-15765	198	8	camera	camera	NOUN
fcis-15765	198	9	pose	pose	NOUN
fcis-15765	198	10	and	and	CCONJ
fcis-15765	198	11	depth	depth	NOUN
fcis-15765	198	12	map	map	NOUN
fcis-15765	198	13	from	from	ADP
fcis-15765	198	14	the	the	DET
fcis-15765	198	15	first	first	ADJ
fcis-15765	198	16	image	image	NOUN
fcis-15765	198	17	.	.	PUNCT
fcis-15765	199	1	bozorgtabar	bozorgtabar	PROPN
fcis-15765	199	2	et	et	PROPN
fcis-15765	199	3	al	al	PROPN
fcis-15765	199	4	.	.	PUNCT
fcis-15765	200	1	[	[	X
fcis-15765	200	2	48	48	NUM
fcis-15765	200	3	]	]	PUNCT
fcis-15765	200	4	align	align	ADJ
fcis-15765	200	5	monocular	monocular	ADJ
fcis-15765	200	6	depth	depth	NOUN
fcis-15765	200	7	estimates	estimate	NOUN
fcis-15765	200	8	trained	train	VERB
fcis-15765	200	9	on	on	ADP
fcis-15765	200	10	unlabeled	unlabeled	ADJ
fcis-15765	200	11	monocular	monocular	ADJ
fcis-15765	200	12	videos	video	NOUN
fcis-15765	200	13	with	with	ADP
fcis-15765	200	14	the	the	DET
fcis-15765	200	15	depth	depth	NOUN
fcis-15765	200	16	features	feature	NOUN
fcis-15765	200	17	of	of	ADP
fcis-15765	200	18	synthetic	synthetic	ADJ
fcis-15765	200	19	images	image	NOUN
fcis-15765	200	20	to	to	PART
fcis-15765	200	21	resolve	resolve	VERB
fcis-15765	200	22	scale	scale	NOUN
fcis-15765	200	23	ambiguities	ambiguity	NOUN
fcis-15765	200	24	.	.	PUNCT
fcis-15765	201	1	these	these	DET
fcis-15765	201	2	deep	deep	ADJ
fcis-15765	201	3	features	feature	NOUN
fcis-15765	201	4	are	be	AUX
fcis-15765	201	5	combined	combine	VERB
fcis-15765	201	6	with	with	ADP
fcis-15765	201	7	scene	scene	NOUN
fcis-15765	201	8	depth	depth	NOUN
fcis-15765	201	9	information	information	NOUN
fcis-15765	201	10	.	.	PUNCT
fcis-15765	202	1	inspired	inspire	VERB
fcis-15765	202	2	by	by	ADP
fcis-15765	202	3	[	[	X
fcis-15765	202	4	14	14	NUM
fcis-15765	202	5	]	]	X
fcis-15765	202	6	,	,	PUNCT
fcis-15765	202	7	prasad	prasad	NOUN
fcis-15765	202	8	and	and	CCONJ
fcis-15765	202	9	bhowmich	bhowmich	VERB
fcis-15765	202	10	[	[	X
fcis-15765	202	11	50	50	NUM
fcis-15765	202	12	]	]	PUNCT
fcis-15765	202	13	use	use	NOUN
fcis-15765	202	14	pole	pole	NOUN
fcis-15765	202	15	constraints	constraint	NOUN
fcis-15765	202	16	to	to	PART
fcis-15765	202	17	optimize	optimize	VERB
fcis-15765	202	18	joint	joint	ADJ
fcis-15765	202	19	learning	learning	NOUN
fcis-15765	202	20	of	of	ADP
fcis-15765	202	21	depth	depth	NOUN
fcis-15765	202	22	and	and	CCONJ
fcis-15765	202	23	ego	ego	NOUN
fcis-15765	202	24	-	-	PUNCT
fcis-15765	202	25	motion	motion	NOUN
fcis-15765	202	26	.	.	PUNCT
fcis-15765	203	1	the	the	DET
fcis-15765	203	2	main	main	ADJ
fcis-15765	203	3	idea	idea	NOUN
fcis-15765	203	4	behind	behind	ADP
fcis-15765	203	5	the	the	DET
fcis-15765	203	6	training	training	NOUN
fcis-15765	203	7	is	be	AUX
fcis-15765	203	8	similar	similar	ADJ
fcis-15765	203	9	to	to	ADP
fcis-15765	203	10	[	[	X
fcis-15765	203	11	49	49	NUM
fcis-15765	203	12	]	]	PUNCT
fcis-15765	203	13	.	.	PUNCT
fcis-15765	204	1	instead	instead	ADV
fcis-15765	204	2	of	of	ADP
fcis-15765	204	3	using	use	VERB
fcis-15765	204	4	epipolar	epipolar	ADJ
fcis-15765	204	5	constraints	constraint	NOUN
fcis-15765	204	6	as	as	ADP
fcis-15765	204	7	training	training	NOUN
fcis-15765	204	8	labels	label	NOUN
fcis-15765	204	9	,	,	PUNCT
fcis-15765	204	10	the	the	DET
fcis-15765	204	11	authors	author	NOUN
fcis-15765	204	12	apply	apply	VERB
fcis-15765	204	13	it	it	PRON
fcis-15765	204	14	to	to	ADP
fcis-15765	204	15	weight	weight	NOUN
fcis-15765	204	16	pixels	pixel	NOUN
fcis-15765	204	17	to	to	PART
fcis-15765	204	18	guide	guide	VERB
fcis-15765	204	19	training	training	NOUN
fcis-15765	204	20	.	.	PUNCT
fcis-15765	205	1	klodt	klodt	NOUN
fcis-15765	205	2	and	and	CCONJ
fcis-15765	205	3	vedaldi	vedaldi	PROPN
fcis-15765	205	4	[	[	X
fcis-15765	205	5	51	51	NUM
fcis-15765	205	6	]	]	PUNCT
fcis-15765	205	7	modified	modify	VERB
fcis-15765	205	8	[	[	X
fcis-15765	205	9	14	14	NUM
fcis-15765	205	10	]	]	PUNCT
fcis-15765	205	11	in	in	ADP
fcis-15765	205	12	the	the	DET
fcis-15765	205	13	following	following	ADJ
fcis-15765	205	14	ways	way	NOUN
fcis-15765	205	15	.	.	PUNCT
fcis-15765	206	1	first	first	ADV
fcis-15765	206	2	,	,	PUNCT
fcis-15765	206	3	a	a	DET
fcis-15765	206	4	structural	structural	ADJ
fcis-15765	206	5	similarity	similarity	NOUN
fcis-15765	206	6	loss	loss	NOUN
fcis-15765	206	7	is	be	AUX
fcis-15765	206	8	introduced	introduce	VERB
fcis-15765	206	9	to	to	PART
fcis-15765	206	10	enhance	enhance	VERB
fcis-15765	206	11	the	the	DET
fcis-15765	206	12	brightness	brightness	NOUN
fcis-15765	206	13	constancy	constancy	NOUN
fcis-15765	206	14	loss	loss	NOUN
fcis-15765	206	15	.	.	PUNCT
fcis-15765	207	1	furthermore	furthermore	ADV
fcis-15765	207	2	,	,	PUNCT
fcis-15765	207	3	an	an	DET
fcis-15765	207	4	explicit	explicit	ADJ
fcis-15765	207	5	confidence	confidence	NOUN
fcis-15765	207	6	model	model	NOUN
fcis-15765	207	7	is	be	AUX
fcis-15765	207	8	incorporated	incorporate	VERB
fcis-15765	207	9	into	into	ADP
fcis-15765	207	10	the	the	DET
fcis-15765	207	11	network	network	NOUN
fcis-15765	207	12	by	by	ADP
fcis-15765	207	13	predicting	predict	VERB
fcis-15765	207	14	the	the	DET
fcis-15765	207	15	likely	likely	ADJ
fcis-15765	207	16	brightness	brightness	NOUN
fcis-15765	207	17	distribution	distribution	NOUN
fcis-15765	207	18	of	of	ADP
fcis-15765	207	19	each	each	DET
fcis-15765	207	20	pixel	pixel	NOUN
fcis-15765	207	21	.	.	PUNCT
fcis-15765	208	1	finally	finally	ADV
fcis-15765	208	2	,	,	PUNCT
fcis-15765	208	3	the	the	DET
fcis-15765	208	4	sfm	sfm	PROPN
fcis-15765	208	5	algorithm	algorithm	NOUN
fcis-15765	208	6	[	[	X
fcis-15765	208	7	52	52	NUM
fcis-15765	208	8	]	]	PUNCT
fcis-15765	208	9	is	be	AUX
fcis-15765	208	10	applied	apply	VERB
fcis-15765	208	11	to	to	ADP
fcis-15765	208	12	the	the	DET
fcis-15765	208	13	network	network	NOUN
fcis-15765	208	14	to	to	PART
fcis-15765	208	15	provide	provide	VERB
fcis-15765	208	16	supervisory	supervisory	ADJ
fcis-15765	208	17	signals	signal	NOUN
fcis-15765	208	18	for	for	ADP
fcis-15765	208	19	the	the	DET
fcis-15765	208	20	training	training	NOUN
fcis-15765	208	21	of	of	ADP
fcis-15765	208	22	the	the	DET
fcis-15765	208	23	depth	depth	NOUN
fcis-15765	208	24	estimation	estimation	NOUN
fcis-15765	208	25	network	network	NOUN
fcis-15765	208	26	.	.	PUNCT
fcis-15765	209	1	in	in	ADP
fcis-15765	209	2	addition	addition	NOUN
fcis-15765	209	3	to	to	ADP
fcis-15765	209	4	learning	learn	VERB
fcis-15765	209	5	depth	depth	NOUN
fcis-15765	209	6	from	from	ADP
fcis-15765	209	7	view	view	NOUN
fcis-15765	209	8	synthesis	synthesis	NOUN
fcis-15765	209	9	or	or	CCONJ
fcis-15765	209	10	minimizing	minimize	VERB
fcis-15765	209	11	photometric	photometric	ADJ
fcis-15765	209	12	reconstruction	reconstruction	NOUN
fcis-15765	209	13	errors	error	NOUN
fcis-15765	209	14	,	,	PUNCT
fcis-15765	209	15	generative	generative	ADJ
fcis-15765	209	16	adversarial	adversarial	ADJ
fcis-15765	209	17	networks	network	NOUN
fcis-15765	209	18	(	(	PUNCT
fcis-15765	209	19	gans	gan	NOUN
fcis-15765	209	20	)	)	PUNCT
fcis-15765	209	21	also	also	ADV
fcis-15765	209	22	address	address	VERB
fcis-15765	209	23	the	the	DET
fcis-15765	209	24	problem	problem	NOUN
fcis-15765	209	25	of	of	ADP
fcis-15765	209	26	unsupervised	unsupervised	ADJ
fcis-15765	209	27	monocular	monocular	ADJ
fcis-15765	209	28	depth	depth	NOUN
fcis-15765	209	29	estimation	estimation	NOUN
fcis-15765	209	30	.	.	PUNCT
fcis-15765	210	1	gan	gan	PROPN
fcis-15765	210	2	consists	consist	VERB
fcis-15765	210	3	of	of	ADP
fcis-15765	210	4	a	a	DET
fcis-15765	210	5	generator	generator	NOUN
fcis-15765	210	6	network	network	NOUN
fcis-15765	210	7	and	and	CCONJ
fcis-15765	210	8	a	a	DET
fcis-15765	210	9	discriminator	discriminator	NOUN
fcis-15765	210	10	network	network	NOUN
fcis-15765	210	11	.	.	PUNCT
fcis-15765	211	1	these	these	DET
fcis-15765	211	2	two	two	NUM
fcis-15765	211	3	networks	network	NOUN
fcis-15765	211	4	are	be	AUX
fcis-15765	211	5	trained	train	VERB
fcis-15765	211	6	through	through	ADP
fcis-15765	211	7	the	the	DET
fcis-15765	211	8	backpropagation	backpropagation	NOUN
fcis-15765	211	9	algorithm	algorithm	NOUN
fcis-15765	211	10	,	,	PUNCT
fcis-15765	211	11	so	so	SCONJ
fcis-15765	211	12	they	they	PRON
fcis-15765	211	13	can	can	AUX
fcis-15765	211	14	work	work	VERB
fcis-15765	211	15	together	together	ADV
fcis-15765	211	16	to	to	PART
fcis-15765	211	17	build	build	VERB
fcis-15765	211	18	an	an	DET
fcis-15765	211	19	unsupervised	unsupervised	ADJ
fcis-15765	211	20	learning	learning	NOUN
fcis-15765	211	21	model	model	NOUN
fcis-15765	211	22	.	.	PUNCT
fcis-15765	212	1	since	since	SCONJ
fcis-15765	212	2	there	there	PRON
fcis-15765	212	3	is	be	VERB
fcis-15765	212	4	no	no	DET
fcis-15765	212	5	true	true	ADJ
fcis-15765	212	6	depth	depth	NOUN
fcis-15765	212	7	in	in	ADP
fcis-15765	212	8	unsupervised	unsupervised	ADJ
fcis-15765	212	9	learning	learning	NOUN
fcis-15765	212	10	,	,	PUNCT
fcis-15765	212	11	the	the	DET
fcis-15765	212	12	discriminator	discriminator	NOUN
fcis-15765	212	13	distinguishes	distinguish	VERB
fcis-15765	212	14	synthetic	synthetic	ADJ
fcis-15765	212	15	images	image	NOUN
fcis-15765	212	16	from	from	ADP
fcis-15765	212	17	real	real	ADJ
fcis-15765	212	18	images	image	NOUN
fcis-15765	212	19	.	.	PUNCT
fcis-15765	213	1	aleotti	aleotti	VERB
fcis-15765	213	2	et	et	PROPN
fcis-15765	213	3	al	al	PROPN
fcis-15765	213	4	.	.	PUNCT
fcis-15765	214	1	[	[	X
fcis-15765	214	2	53	53	NUM
fcis-15765	214	3	]	]	PUNCT
fcis-15765	214	4	proposed	propose	VERB
fcis-15765	214	5	the	the	DET
fcis-15765	214	6	first	first	ADJ
fcis-15765	214	7	generative	generative	ADJ
fcis-15765	214	8	adversarial	adversarial	ADJ
fcis-15765	214	9	network	network	NOUN
fcis-15765	214	10	for	for	ADP
fcis-15765	214	11	unsupervised	unsupervised	ADJ
fcis-15765	214	12	monocular	monocular	ADJ
fcis-15765	214	13	depth	depth	NOUN
fcis-15765	214	14	estimation	estimation	NOUN
fcis-15765	214	15	.	.	PUNCT
fcis-15765	215	1	a	a	DET
fcis-15765	215	2	generator	generator	NOUN
fcis-15765	215	3	network	network	NOUN
fcis-15765	215	4	is	be	AUX
fcis-15765	215	5	trained	train	VERB
fcis-15765	215	6	to	to	PART
fcis-15765	215	7	infer	infer	VERB
fcis-15765	215	8	a	a	DET
fcis-15765	215	9	depth	depth	NOUN
fcis-15765	215	10	map	map	NOUN
fcis-15765	215	11	from	from	ADP
fcis-15765	215	12	input	input	NOUN
fcis-15765	215	13	images	image	NOUN
fcis-15765	215	14	to	to	PART
fcis-15765	215	15	generate	generate	VERB
fcis-15765	215	16	warped	warped	ADJ
fcis-15765	215	17	synthetic	synthetic	ADJ
fcis-15765	215	18	images	image	NOUN
fcis-15765	215	19	.	.	PUNCT
fcis-15765	216	1	train	train	VERB
fcis-15765	216	2	a	a	DET
fcis-15765	216	3	discriminator	discriminator	NOUN
fcis-15765	216	4	network	network	NOUN
fcis-15765	216	5	to	to	PART
fcis-15765	216	6	distinguish	distinguish	VERB
fcis-15765	216	7	distorted	distort	VERB
fcis-15765	216	8	images	image	NOUN
fcis-15765	216	9	from	from	ADP
fcis-15765	216	10	input	input	ADJ
fcis-15765	216	11	real	real	ADJ
fcis-15765	216	12	images	image	NOUN
fcis-15765	216	13	.	.	PUNCT
fcis-15765	217	1	since	since	SCONJ
fcis-15765	217	2	the	the	DET
fcis-15765	217	3	quality	quality	NOUN
fcis-15765	217	4	of	of	ADP
fcis-15765	217	5	the	the	DET
fcis-15765	217	6	estimated	estimate	VERB
fcis-15765	217	7	depth	depth	NOUN
fcis-15765	217	8	map	map	NOUN
fcis-15765	217	9	has	have	VERB
fcis-15765	217	10	an	an	DET
fcis-15765	217	11	impact	impact	NOUN
fcis-15765	217	12	on	on	ADP
fcis-15765	217	13	the	the	DET
fcis-15765	217	14	distorted	distorted	ADJ
fcis-15765	217	15	synthetic	synthetic	ADJ
fcis-15765	217	16	image	image	NOUN
fcis-15765	217	17	,	,	PUNCT
fcis-15765	217	18	the	the	DET
fcis-15765	217	19	generator	generator	NOUN
fcis-15765	217	20	is	be	AUX
fcis-15765	217	21	forced	force	VERB
fcis-15765	217	22	to	to	PART
fcis-15765	217	23	produce	produce	VERB
fcis-15765	217	24	a	a	DET
fcis-15765	217	25	more	more	ADV
fcis-15765	217	26	accurate	accurate	ADJ
fcis-15765	217	27	depth	depth	NOUN
fcis-15765	217	28	map	map	NOUN
fcis-15765	217	29	.	.	PUNCT
fcis-15765	218	1	mehta	mehta	PROPN
fcis-15765	218	2	et	et	PROPN
fcis-15765	218	3	al	al	PROPN
fcis-15765	218	4	.	.	PUNCT
fcis-15765	219	1	[	[	X
fcis-15765	219	2	54	54	NUM
fcis-15765	219	3	]	]	PUNCT
fcis-15765	219	4	introduced	introduce	VERB
fcis-15765	219	5	a	a	DET
fcis-15765	219	6	structural	structural	ADJ
fcis-15765	219	7	adversarial	adversarial	ADJ
fcis-15765	219	8	training	training	NOUN
fcis-15765	219	9	method	method	NOUN
fcis-15765	219	10	that	that	PRON
fcis-15765	219	11	uses	use	VERB
fcis-15765	219	12	stereo	stereo	ADJ
fcis-15765	219	13	view	view	NOUN
fcis-15765	219	14	synthesis	synthesis	NOUN
fcis-15765	219	15	to	to	PART
fcis-15765	219	16	predict	predict	VERB
fcis-15765	219	17	dense	dense	ADJ
fcis-15765	219	18	depth	depth	NOUN
fcis-15765	219	19	maps	map	NOUN
fcis-15765	219	20	.	.	PUNCT
fcis-15765	220	1	given	give	VERB
fcis-15765	220	2	a	a	DET
fcis-15765	220	3	monocular	monocular	ADJ
fcis-15765	220	4	image	image	NOUN
fcis-15765	220	5	,	,	PUNCT
fcis-15765	220	6	the	the	DET
fcis-15765	220	7	generator	generator	NOUN
fcis-15765	220	8	network	network	NOUN
fcis-15765	220	9	outputs	output	VERB
fcis-15765	220	10	a	a	DET
fcis-15765	220	11	dense	dense	ADJ
fcis-15765	220	12	disparity	disparity	NOUN
fcis-15765	220	13	map	map	NOUN
fcis-15765	220	14	.	.	PUNCT
fcis-15765	221	1	using	use	VERB
fcis-15765	221	2	the	the	DET
fcis-15765	221	3	resulting	result	VERB
fcis-15765	221	4	disparity	disparity	NOUN
fcis-15765	221	5	map	map	NOUN
fcis-15765	221	6	,	,	PUNCT
fcis-15765	221	7	a	a	DET
fcis-15765	221	8	multi	multi	ADJ
fcis-15765	221	9	-	-	ADJ
fcis-15765	221	10	view	view	ADJ
fcis-15765	221	11	stereo	stereo	NOUN
fcis-15765	221	12	pair	pair	NOUN
fcis-15765	221	13	corresponding	correspond	VERB
fcis-15765	221	14	to	to	ADP
fcis-15765	221	15	the	the	DET
fcis-15765	221	16	input	input	NOUN
fcis-15765	221	17	image	image	NOUN
fcis-15765	221	18	views	view	NOUN
fcis-15765	221	19	is	be	AUX
fcis-15765	221	20	generated	generate	VERB
fcis-15765	221	21	.	.	PUNCT
fcis-15765	222	1	a	a	DET
fcis-15765	222	2	discriminator	discriminator	NOUN
fcis-15765	222	3	network	network	NOUN
fcis-15765	222	4	distinguishes	distinguish	VERB
fcis-15765	222	5	these	these	DET
fcis-15765	222	6	reconstructed	reconstruct	VERB
fcis-15765	222	7	views	view	NOUN
fcis-15765	222	8	from	from	ADP
fcis-15765	222	9	the	the	DET
fcis-15765	222	10	real	real	ADJ
fcis-15765	222	11	ones	one	NOUN
fcis-15765	222	12	in	in	ADP
fcis-15765	222	13	the	the	DET
fcis-15765	222	14	training	training	NOUN
fcis-15765	222	15	data	datum	NOUN
fcis-15765	222	16	.	.	PUNCT
fcis-15765	223	1	fig	fig	NOUN
fcis-15765	223	2	2	2	NUM
fcis-15765	223	3	.	.	NUM
fcis-15765	223	4	developed	develop	VERB
fcis-15765	223	5	network	network	NOUN
fcis-15765	223	6	by	by	ADP
fcis-15765	223	7	zhou	zhou	PROPN
fcis-15765	223	8	et	et	PROPN
fcis-15765	223	9	al	al	PROPN
fcis-15765	223	10	.	.	PUNCT
fcis-15765	224	1	[	[	X
fcis-15765	224	2	14	14	NUM
fcis-15765	224	3	]	]	SYM
fcis-15765	224	4	6	6	NUM
fcis-15765	224	5	.	.	X
fcis-15765	224	6	conclusion	conclusion	NOUN
fcis-15765	224	7	and	and	CCONJ
fcis-15765	224	8	future	future	ADJ
fcis-15765	224	9	developments	development	NOUN
fcis-15765	224	10	deep	deep	ADJ
fcis-15765	224	11	learning	learning	NOUN
fcis-15765	224	12	techniques	technique	NOUN
fcis-15765	224	13	have	have	VERB
fcis-15765	224	14	great	great	ADJ
fcis-15765	224	15	potential	potential	NOUN
fcis-15765	224	16	for	for	ADP
fcis-15765	224	17	predicting	predict	VERB
fcis-15765	224	18	depth	depth	NOUN
fcis-15765	224	19	in	in	ADP
fcis-15765	224	20	monocular	monocular	ADJ
fcis-15765	224	21	images	image	NOUN
fcis-15765	224	22	.	.	PUNCT
fcis-15765	225	1	depth	depth	NOUN
fcis-15765	225	2	prediction	prediction	NOUN
fcis-15765	225	3	for	for	ADP
fcis-15765	225	4	monocular	monocular	ADJ
fcis-15765	225	5	images	image	NOUN
fcis-15765	225	6	can	can	AUX
fcis-15765	225	7	be	be	AUX
fcis-15765	225	8	achieved	achieve	VERB
fcis-15765	225	9	using	use	VERB
fcis-15765	225	10	an	an	DET
fcis-15765	225	11	efficient	efficient	ADJ
fcis-15765	225	12	deep	deep	ADJ
fcis-15765	225	13	learning	learning	NOUN
fcis-15765	225	14	network	network	NOUN
fcis-15765	225	15	structure	structure	NOUN
fcis-15765	225	16	and	and	CCONJ
fcis-15765	225	17	a	a	DET
fcis-15765	225	18	dataset	dataset	NOUN
fcis-15765	225	19	suitable	suitable	ADJ
fcis-15765	225	20	for	for	ADP
fcis-15765	225	21	the	the	DET
fcis-15765	225	22	learning	learning	NOUN
fcis-15765	225	23	technique	technique	NOUN
fcis-15765	225	24	.	.	PUNCT
fcis-15765	226	1	this	this	DET
fcis-15765	226	2	paper	paper	NOUN
fcis-15765	226	3	provides	provide	VERB
fcis-15765	226	4	a	a	DET
fcis-15765	226	5	brief	brief	ADJ
fcis-15765	226	6	overview	overview	NOUN
fcis-15765	226	7	of	of	ADP
fcis-15765	226	8	the	the	DET
fcis-15765	226	9	contributions	contribution	NOUN
fcis-15765	226	10	of	of	ADP
fcis-15765	226	11	this	this	DET
fcis-15765	226	12	growing	grow	VERB
fcis-15765	226	13	scientific	scientific	ADJ
fcis-15765	226	14	field	field	NOUN
fcis-15765	226	15	in	in	ADP
fcis-15765	226	16	deep	deep	ADJ
fcis-15765	226	17	learning	learning	NOUN
fcis-15765	226	18	-	-	PUNCT
fcis-15765	226	19	based	base	VERB
fcis-15765	226	20	monocular	monocular	ADJ
fcis-15765	226	21	depth	depth	NOUN
fcis-15765	226	22	estimation	estimation	NOUN
fcis-15765	226	23	.	.	PUNCT
fcis-15765	227	1	and	and	CCONJ
fcis-15765	227	2	the	the	DET
fcis-15765	227	3	research	research	NOUN
fcis-15765	227	4	on	on	ADP
fcis-15765	227	5	monocular	monocular	ADJ
fcis-15765	227	6	depth	depth	NOUN
fcis-15765	227	7	estimation	estimation	NOUN
fcis-15765	227	8	is	be	AUX
fcis-15765	227	9	reviewed	review	VERB
fcis-15765	227	10	from	from	ADP
fcis-15765	227	11	different	different	ADJ
fcis-15765	227	12	32	32	NUM
fcis-15765	227	13	aspects	aspect	NOUN
fcis-15765	227	14	,	,	PUNCT
fcis-15765	227	15	including	include	VERB
fcis-15765	227	16	training	train	VERB
fcis-15765	227	17	data	datum	NOUN
fcis-15765	227	18	sets	set	NOUN
fcis-15765	227	19	,	,	PUNCT
fcis-15765	227	20	supervised	supervised	ADJ
fcis-15765	227	21	and	and	CCONJ
fcis-15765	227	22	unsupervised	unsupervised	ADJ
fcis-15765	227	23	methods	method	NOUN
fcis-15765	227	24	,	,	PUNCT
fcis-15765	227	25	and	and	CCONJ
fcis-15765	227	26	we	we	PRON
fcis-15765	227	27	also	also	ADV
fcis-15765	227	28	introduce	introduce	VERB
fcis-15765	227	29	evaluation	evaluation	NOUN
fcis-15765	227	30	indicators	indicator	NOUN
fcis-15765	227	31	.	.	PUNCT
fcis-15765	228	1	for	for	ADP
fcis-15765	228	2	the	the	DET
fcis-15765	228	3	development	development	NOUN
fcis-15765	228	4	of	of	ADP
fcis-15765	228	5	monocular	monocular	ADJ
fcis-15765	228	6	depth	depth	NOUN
fcis-15765	228	7	estimation	estimation	NOUN
fcis-15765	228	8	,	,	PUNCT
fcis-15765	228	9	from	from	ADP
fcis-15765	228	10	a	a	DET
fcis-15765	228	11	future	future	ADJ
fcis-15765	228	12	perspective	perspective	NOUN
fcis-15765	228	13	,	,	PUNCT
fcis-15765	228	14	the	the	DET
fcis-15765	228	15	architecture	architecture	NOUN
fcis-15765	228	16	of	of	ADP
fcis-15765	228	17	deep	deep	ADJ
fcis-15765	228	18	learning	learning	NOUN
fcis-15765	228	19	models	model	NOUN
fcis-15765	228	20	must	must	AUX
fcis-15765	228	21	be	be	AUX
fcis-15765	228	22	improved	improve	VERB
fcis-15765	228	23	to	to	PART
fcis-15765	228	24	improve	improve	VERB
fcis-15765	228	25	the	the	DET
fcis-15765	228	26	accuracy	accuracy	NOUN
fcis-15765	228	27	and	and	CCONJ
fcis-15765	228	28	reliability	reliability	NOUN
fcis-15765	228	29	of	of	ADP
fcis-15765	228	30	the	the	DET
fcis-15765	228	31	proposed	propose	VERB
fcis-15765	228	32	network	network	NOUN
fcis-15765	228	33	and	and	CCONJ
fcis-15765	228	34	reduce	reduce	VERB
fcis-15765	228	35	its	its	PRON
fcis-15765	228	36	inference	inference	NOUN
fcis-15765	228	37	time	time	NOUN
fcis-15765	228	38	.	.	PUNCT
fcis-15765	229	1	at	at	ADP
fcis-15765	229	2	the	the	DET
fcis-15765	229	3	same	same	ADJ
fcis-15765	229	4	time	time	NOUN
fcis-15765	229	5	,	,	PUNCT
fcis-15765	229	6	a	a	DET
fcis-15765	229	7	data	datum	NOUN
fcis-15765	229	8	set	set	VERB
fcis-15765	229	9	composed	compose	VERB
fcis-15765	229	10	of	of	ADP
fcis-15765	229	11	various	various	ADJ
fcis-15765	229	12	scenes	scene	NOUN
fcis-15765	229	13	is	be	AUX
fcis-15765	229	14	also	also	ADV
fcis-15765	229	15	needed	need	VERB
fcis-15765	229	16	so	so	SCONJ
fcis-15765	229	17	that	that	SCONJ
fcis-15765	229	18	the	the	DET
fcis-15765	229	19	model	model	NOUN
fcis-15765	229	20	can	can	AUX
fcis-15765	229	21	learn	learn	VERB
fcis-15765	229	22	various	various	ADJ
fcis-15765	229	23	scene	scene	NOUN
fcis-15765	229	24	features	feature	NOUN
fcis-15765	229	25	.	.	PUNCT
fcis-15765	230	1	therefore	therefore	ADV
fcis-15765	230	2	,	,	PUNCT
fcis-15765	230	3	how	how	SCONJ
fcis-15765	230	4	to	to	PART
fcis-15765	230	5	construct	construct	VERB
fcis-15765	230	6	a	a	DET
fcis-15765	230	7	data	data	NOUN
fcis-15765	230	8	set	set	VERB
fcis-15765	230	9	that	that	SCONJ
fcis-15765	230	10	satisfies	satisfie	NOUN
fcis-15765	230	11	deep	deep	ADJ
fcis-15765	230	12	learning	learning	NOUN
fcis-15765	230	13	has	have	AUX
fcis-15765	230	14	become	become	VERB
fcis-15765	230	15	an	an	DET
fcis-15765	230	16	important	important	ADJ
fcis-15765	230	17	research	research	NOUN
fcis-15765	230	18	direction	direction	NOUN
fcis-15765	230	19	.	.	PUNCT
fcis-15765	231	1	references	reference	NOUN
fcis-15765	231	2	[	[	X
fcis-15765	231	3	1	1	NUM
fcis-15765	231	4	]	]	X
fcis-15765	231	5	zhou	zhou	PROPN
fcis-15765	231	6	b	b	PROPN
fcis-15765	231	7	,	,	PUNCT
fcis-15765	231	8	krähenbühl	krähenbühl	ADJ
fcis-15765	231	9	p	p	NOUN
fcis-15765	231	10	,	,	PUNCT
fcis-15765	231	11	koltun	koltun	PROPN
fcis-15765	231	12	v.	v.	PROPN
fcis-15765	231	13	does	do	AUX
fcis-15765	231	14	computer	computer	NOUN
fcis-15765	231	15	vision	vision	NOUN
fcis-15765	231	16	matter	matter	VERB
fcis-15765	231	17	for	for	ADP
fcis-15765	231	18	action?[j	action?[j	X
fcis-15765	231	19	]	]	PUNCT
fcis-15765	231	20	.	.	PUNCT
fcis-15765	232	1	science	science	NOUN
fcis-15765	232	2	robotics	robotic	NOUN
fcis-15765	232	3	,	,	PUNCT
fcis-15765	232	4	2019	2019	NUM
fcis-15765	232	5	,	,	PUNCT
fcis-15765	232	6	4(30	4(30	NUM
fcis-15765	232	7	):	):	PUNCT
fcis-15765	232	8	eaaw6661	eaaw6661	ADJ
fcis-15765	232	9	.	.	PUNCT
fcis-15765	233	1	[	[	X
fcis-15765	233	2	2	2	X
fcis-15765	233	3	]	]	X
fcis-15765	233	4	geiger	geiger	PROPN
fcis-15765	233	5	a	a	PROPN
fcis-15765	233	6	,	,	PUNCT
fcis-15765	233	7	lenz	lenz	PROPN
fcis-15765	233	8	p	p	X
fcis-15765	233	9	,	,	PUNCT
fcis-15765	233	10	urtasun	urtasun	PROPN
fcis-15765	233	11	r.	r.	PROPN
fcis-15765	233	12	are	be	AUX
fcis-15765	233	13	we	we	PRON
fcis-15765	233	14	ready	ready	ADJ
fcis-15765	233	15	for	for	ADP
fcis-15765	233	16	autonomous	autonomous	ADJ
fcis-15765	233	17	driving	driving	NOUN
fcis-15765	233	18	?	?	PUNCT
fcis-15765	234	1	the	the	DET
fcis-15765	234	2	kitti	kitti	PROPN
fcis-15765	234	3	vision	vision	PROPN
fcis-15765	234	4	benchmark	benchmark	PROPN
fcis-15765	234	5	suite[c]//2012	suite[c]//2012	PROPN
fcis-15765	234	6	ieee	ieee	NOUN
fcis-15765	234	7	conference	conference	NOUN
fcis-15765	234	8	on	on	ADP
fcis-15765	234	9	computer	computer	NOUN
fcis-15765	234	10	vision	vision	NOUN
fcis-15765	234	11	and	and	CCONJ
fcis-15765	234	12	pattern	pattern	NOUN
fcis-15765	234	13	recognition	recognition	NOUN
fcis-15765	234	14	.	.	PUNCT
fcis-15765	235	1	ieee	ieee	NOUN
fcis-15765	235	2	,	,	PUNCT
fcis-15765	235	3	2012	2012	NUM
fcis-15765	235	4	:	:	PUNCT
fcis-15765	235	5	3354	3354	NUM
fcis-15765	235	6	-	-	SYM
fcis-15765	235	7	3361	3361	NUM
fcis-15765	235	8	.	.	PUNCT
fcis-15765	236	1	[	[	X
fcis-15765	236	2	3	3	X
fcis-15765	236	3	]	]	X
fcis-15765	236	4	kazmi	kazmi	PROPN
fcis-15765	236	5	w	w	PROPN
fcis-15765	236	6	,	,	PUNCT
fcis-15765	236	7	foix	foix	PROPN
fcis-15765	236	8	s	s	PROPN
fcis-15765	236	9	,	,	PUNCT
fcis-15765	236	10	alenyà	alenyà	PROPN
fcis-15765	236	11	g	g	PROPN
fcis-15765	236	12	,	,	PUNCT
fcis-15765	236	13	et	et	PROPN
fcis-15765	236	14	al	al	PROPN
fcis-15765	236	15	.	.	PROPN
fcis-15765	236	16	indoor	indoor	ADJ
fcis-15765	236	17	and	and	CCONJ
fcis-15765	236	18	outdoor	outdoor	ADJ
fcis-15765	236	19	depth	depth	NOUN
fcis-15765	236	20	imaging	imaging	NOUN
fcis-15765	236	21	of	of	ADP
fcis-15765	236	22	leaves	leave	NOUN
fcis-15765	236	23	with	with	ADP
fcis-15765	236	24	time	time	NOUN
fcis-15765	236	25	-	-	PUNCT
fcis-15765	236	26	of	of	ADP
fcis-15765	236	27	-	-	PUNCT
fcis-15765	236	28	flight	flight	NOUN
fcis-15765	236	29	and	and	CCONJ
fcis-15765	236	30	stereo	stereo	NOUN
fcis-15765	236	31	vision	vision	NOUN
fcis-15765	236	32	sensors	sensor	NOUN
fcis-15765	236	33	:	:	PUNCT
fcis-15765	236	34	analysis	analysis	NOUN
fcis-15765	236	35	and	and	CCONJ
fcis-15765	236	36	comparison[j	comparison[j	NOUN
fcis-15765	236	37	]	]	PUNCT
fcis-15765	236	38	.	.	PUNCT
fcis-15765	237	1	isprs	isprs	PROPN
fcis-15765	237	2	journal	journal	PROPN
fcis-15765	237	3	of	of	ADP
fcis-15765	237	4	photogrammetry	photogrammetry	NOUN
fcis-15765	237	5	and	and	CCONJ
fcis-15765	237	6	remote	remote	ADJ
fcis-15765	237	7	sensing	sensing	NOUN
fcis-15765	237	8	,	,	PUNCT
fcis-15765	237	9	2014	2014	NUM
fcis-15765	237	10	,	,	PUNCT
fcis-15765	237	11	88	88	NUM
fcis-15765	237	12	:	:	SYM
fcis-15765	237	13	128	128	NUM
fcis-15765	237	14	-	-	SYM
fcis-15765	237	15	146	146	NUM
fcis-15765	237	16	.	.	PUNCT
fcis-15765	238	1	[	[	X
fcis-15765	238	2	4	4	NUM
fcis-15765	238	3	]	]	X
fcis-15765	238	4	wöhler	wöhler	PROPN
fcis-15765	238	5	c	c	PROPN
fcis-15765	238	6	,	,	PUNCT
fcis-15765	238	7	d’angelo	d’angelo	X
fcis-15765	238	8	p	p	NOUN
fcis-15765	238	9	,	,	PUNCT
fcis-15765	238	10	krüger	krüger	NOUN
fcis-15765	238	11	l	l	NOUN
fcis-15765	238	12	,	,	PUNCT
fcis-15765	238	13	et	et	PROPN
fcis-15765	238	14	al	al	PROPN
fcis-15765	238	15	.	.	PROPN
fcis-15765	238	16	monocular	monocular	PROPN
fcis-15765	238	17	3d	3d	PROPN
fcis-15765	238	18	scene	scene	NOUN
fcis-15765	238	19	reconstruction	reconstruction	NOUN
fcis-15765	238	20	at	at	ADP
fcis-15765	238	21	absolute	absolute	ADJ
fcis-15765	238	22	scale[j	scale[j	NOUN
fcis-15765	238	23	]	]	PUNCT
fcis-15765	238	24	.	.	PUNCT
fcis-15765	239	1	isprs	isprs	PROPN
fcis-15765	239	2	journal	journal	PROPN
fcis-15765	239	3	of	of	ADP
fcis-15765	239	4	photogrammetry	photogrammetry	NOUN
fcis-15765	239	5	and	and	CCONJ
fcis-15765	239	6	remote	remote	ADJ
fcis-15765	239	7	sensing	sensing	NOUN
fcis-15765	239	8	,	,	PUNCT
fcis-15765	239	9	2009	2009	NUM
fcis-15765	239	10	,	,	PUNCT
fcis-15765	239	11	64(6	64(6	NUM
fcis-15765	239	12	):	):	PUNCT
fcis-15765	239	13	529	529	NUM
fcis-15765	239	14	-	-	SYM
fcis-15765	239	15	540	540	NUM
fcis-15765	239	16	.	.	PUNCT
fcis-15765	240	1	[	[	X
fcis-15765	240	2	5	5	X
fcis-15765	240	3	]	]	X
fcis-15765	240	4	srinivasan	srinivasan	NOUN
fcis-15765	240	5	p	p	PROPN
fcis-15765	240	6	p	p	PROPN
fcis-15765	240	7	,	,	PUNCT
fcis-15765	240	8	garg	garg	NOUN
fcis-15765	240	9	r	r	PROPN
fcis-15765	240	10	,	,	PUNCT
fcis-15765	240	11	wadhwa	wadhwa	PROPN
fcis-15765	240	12	n	n	CCONJ
fcis-15765	240	13	,	,	PUNCT
fcis-15765	240	14	et	et	PROPN
fcis-15765	240	15	al	al	PROPN
fcis-15765	240	16	.	.	PROPN
fcis-15765	240	17	aperture	aperture	PROPN
fcis-15765	240	18	supervision	supervision	NOUN
fcis-15765	240	19	for	for	ADP
fcis-15765	240	20	monocular	monocular	ADJ
fcis-15765	240	21	depth	depth	NOUN
fcis-15765	240	22	estimation[c]//proceedings	estimation[c]//proceeding	NOUN
fcis-15765	240	23	of	of	ADP
fcis-15765	240	24	the	the	DET
fcis-15765	240	25	ieee	ieee	NOUN
fcis-15765	240	26	conference	conference	NOUN
fcis-15765	240	27	on	on	ADP
fcis-15765	240	28	computer	computer	NOUN
fcis-15765	240	29	vision	vision	NOUN
fcis-15765	240	30	and	and	CCONJ
fcis-15765	240	31	pattern	pattern	NOUN
fcis-15765	240	32	recognition	recognition	NOUN
fcis-15765	240	33	.	.	PUNCT
fcis-15765	241	1	2018	2018	NUM
fcis-15765	241	2	:	:	PUNCT
fcis-15765	241	3	6393	6393	NUM
fcis-15765	241	4	-	-	SYM
fcis-15765	241	5	6401	6401	NUM
fcis-15765	241	6	.	.	PUNCT
fcis-15765	242	1	[	[	X
fcis-15765	242	2	6	6	NUM
fcis-15765	242	3	]	]	X
fcis-15765	242	4	hou	hou	PROPN
fcis-15765	242	5	y	y	PROPN
fcis-15765	242	6	,	,	PUNCT
fcis-15765	242	7	peng	peng	PROPN
fcis-15765	242	8	j	j	PROPN
fcis-15765	242	9	,	,	PUNCT
fcis-15765	242	10	hu	hu	PROPN
fcis-15765	243	1	z	z	PROPN
fcis-15765	243	2	,	,	PUNCT
fcis-15765	243	3	et	et	PROPN
fcis-15765	243	4	al	al	PROPN
fcis-15765	243	5	.	.	PUNCT
fcis-15765	244	1	planarity	planarity	NOUN
fcis-15765	244	2	constrained	constrain	VERB
fcis-15765	244	3	multi	multi	ADJ
fcis-15765	244	4	-	-	ADJ
fcis-15765	244	5	view	view	ADJ
fcis-15765	244	6	depth	depth	NOUN
fcis-15765	244	7	map	map	NOUN
fcis-15765	244	8	reconstruction	reconstruction	NOUN
fcis-15765	244	9	for	for	ADP
fcis-15765	244	10	urban	urban	ADJ
fcis-15765	244	11	scenes[j	scenes[j	PROPN
fcis-15765	244	12	]	]	PUNCT
fcis-15765	244	13	.	.	PUNCT
fcis-15765	245	1	isprs	isprs	PROPN
fcis-15765	245	2	journal	journal	PROPN
fcis-15765	245	3	of	of	ADP
fcis-15765	245	4	photogrammetry	photogrammetry	NOUN
fcis-15765	245	5	and	and	CCONJ
fcis-15765	245	6	remote	remote	ADJ
fcis-15765	245	7	sensing	sensing	NOUN
fcis-15765	245	8	,	,	PUNCT
fcis-15765	245	9	2018	2018	NUM
fcis-15765	245	10	,	,	PUNCT
fcis-15765	245	11	139	139	NUM
fcis-15765	245	12	:	:	SYM
fcis-15765	245	13	133	133	NUM
fcis-15765	245	14	-	-	SYM
fcis-15765	245	15	145	145	NUM
fcis-15765	245	16	.	.	PUNCT
fcis-15765	246	1	[	[	X
fcis-15765	246	2	7	7	NUM
fcis-15765	246	3	]	]	X
fcis-15765	246	4	mostegel	mostegel	ADJ
fcis-15765	246	5	c	c	NOUN
fcis-15765	246	6	,	,	PUNCT
fcis-15765	246	7	fraundorfer	fraundorfer	VERB
fcis-15765	246	8	f	f	NUM
fcis-15765	246	9	,	,	PUNCT
fcis-15765	246	10	bischof	bischof	PROPN
fcis-15765	246	11	h.	h.	PROPN
fcis-15765	246	12	prioritized	prioritize	VERB
fcis-15765	246	13	multi	multi	ADJ
fcis-15765	246	14	-	-	ADJ
fcis-15765	246	15	view	view	ADJ
fcis-15765	246	16	stereo	stereo	NOUN
fcis-15765	246	17	depth	depth	NOUN
fcis-15765	246	18	map	map	NOUN
fcis-15765	246	19	generation	generation	NOUN
fcis-15765	246	20	using	use	VERB
fcis-15765	246	21	confidence	confidence	NOUN
fcis-15765	246	22	prediction[j	prediction[j	PROPN
fcis-15765	246	23	]	]	PUNCT
fcis-15765	246	24	.	.	PUNCT
fcis-15765	247	1	isprs	isprs	PROPN
fcis-15765	247	2	journal	journal	PROPN
fcis-15765	247	3	of	of	ADP
fcis-15765	247	4	photogrammetry	photogrammetry	NOUN
fcis-15765	247	5	and	and	CCONJ
fcis-15765	247	6	remote	remote	ADJ
fcis-15765	247	7	sensing	sensing	NOUN
fcis-15765	247	8	,	,	PUNCT
fcis-15765	247	9	2018	2018	NUM
fcis-15765	247	10	,	,	PUNCT
fcis-15765	247	11	143	143	NUM
fcis-15765	247	12	:	:	SYM
fcis-15765	247	13	167	167	NUM
fcis-15765	247	14	-	-	SYM
fcis-15765	247	15	180	180	NUM
fcis-15765	247	16	.	.	PUNCT
fcis-15765	248	1	[	[	X
fcis-15765	248	2	8	8	NUM
fcis-15765	248	3	]	]	PUNCT
fcis-15765	248	4	zeller	zeller	PROPN
fcis-15765	248	5	n	n	PRON
fcis-15765	248	6	,	,	PUNCT
fcis-15765	248	7	quint	quint	PROPN
fcis-15765	248	8	f	f	PROPN
fcis-15765	248	9	,	,	PUNCT
fcis-15765	248	10	stilla	stilla	NOUN
fcis-15765	248	11	u.	u.	NOUN
fcis-15765	248	12	depth	depth	NOUN
fcis-15765	248	13	estimation	estimation	NOUN
fcis-15765	248	14	and	and	CCONJ
fcis-15765	248	15	camera	camera	NOUN
fcis-15765	248	16	calibration	calibration	NOUN
fcis-15765	248	17	of	of	ADP
fcis-15765	248	18	a	a	DET
fcis-15765	248	19	focused	focus	VERB
fcis-15765	248	20	plenoptic	plenoptic	ADJ
fcis-15765	248	21	camera	camera	NOUN
fcis-15765	248	22	for	for	ADP
fcis-15765	248	23	visual	visual	ADJ
fcis-15765	248	24	odometry[j	odometry[j	PROPN
fcis-15765	248	25	]	]	X
fcis-15765	248	26	.	.	PUNCT
fcis-15765	249	1	isprs	isprs	PROPN
fcis-15765	249	2	journal	journal	PROPN
fcis-15765	249	3	of	of	ADP
fcis-15765	249	4	photogrammetry	photogrammetry	NOUN
fcis-15765	249	5	and	and	CCONJ
fcis-15765	249	6	remote	remote	ADJ
fcis-15765	249	7	sensing	sensing	NOUN
fcis-15765	249	8	,	,	PUNCT
fcis-15765	249	9	2016	2016	NUM
fcis-15765	249	10	,	,	PUNCT
fcis-15765	249	11	118	118	NUM
fcis-15765	249	12	:	:	PUNCT
fcis-15765	249	13	83	83	NUM
fcis-15765	249	14	-	-	SYM
fcis-15765	249	15	100	100	NUM
fcis-15765	249	16	.	.	PUNCT
fcis-15765	250	1	[	[	X
fcis-15765	250	2	9	9	NUM
fcis-15765	250	3	]	]	PUNCT
fcis-15765	250	4	he	he	PRON
fcis-15765	250	5	k	k	PROPN
fcis-15765	250	6	,	,	PUNCT
fcis-15765	250	7	zhang	zhang	PROPN
fcis-15765	250	8	x	x	PROPN
fcis-15765	250	9	,	,	PUNCT
fcis-15765	250	10	ren	ren	PROPN
fcis-15765	250	11	s	s	PROPN
fcis-15765	250	12	,	,	PUNCT
fcis-15765	250	13	et	et	PROPN
fcis-15765	250	14	al	al	PROPN
fcis-15765	250	15	.	.	PUNCT
fcis-15765	251	1	deep	deep	ADJ
fcis-15765	251	2	residual	residual	ADJ
fcis-15765	251	3	learning	learning	NOUN
fcis-15765	251	4	for	for	ADP
fcis-15765	251	5	image	image	NOUN
fcis-15765	251	6	recognition[c]//proceedings	recognition[c]//proceeding	NOUN
fcis-15765	251	7	of	of	ADP
fcis-15765	251	8	the	the	DET
fcis-15765	251	9	ieee	ieee	NOUN
fcis-15765	251	10	conference	conference	NOUN
fcis-15765	251	11	on	on	ADP
fcis-15765	251	12	computer	computer	NOUN
fcis-15765	251	13	vision	vision	NOUN
fcis-15765	251	14	and	and	CCONJ
fcis-15765	251	15	pattern	pattern	NOUN
fcis-15765	251	16	recognition	recognition	NOUN
fcis-15765	251	17	.	.	PUNCT
fcis-15765	252	1	2016	2016	NUM
fcis-15765	252	2	:	:	PUNCT
fcis-15765	253	1	770	770	NUM
fcis-15765	253	2	-	-	SYM
fcis-15765	253	3	778	778	NUM
fcis-15765	253	4	.	.	PUNCT
fcis-15765	254	1	[	[	X
fcis-15765	254	2	10	10	NUM
fcis-15765	254	3	]	]	X
fcis-15765	254	4	eigen	eigen	PROPN
fcis-15765	254	5	d	d	PROPN
fcis-15765	254	6	,	,	PUNCT
fcis-15765	254	7	puhrsch	puhrsch	ADV
fcis-15765	254	8	c	c	X
fcis-15765	254	9	,	,	PUNCT
fcis-15765	254	10	fergus	fergus	PROPN
fcis-15765	254	11	r.	r.	PROPN
fcis-15765	254	12	depth	depth	NOUN
fcis-15765	254	13	map	map	NOUN
fcis-15765	254	14	prediction	prediction	NOUN
fcis-15765	254	15	from	from	ADP
fcis-15765	254	16	a	a	DET
fcis-15765	254	17	single	single	ADJ
fcis-15765	254	18	image	image	NOUN
fcis-15765	254	19	using	use	VERB
fcis-15765	254	20	a	a	DET
fcis-15765	254	21	multi	multi	ADJ
fcis-15765	254	22	-	-	ADJ
fcis-15765	254	23	scale	scale	ADJ
fcis-15765	254	24	deep	deep	ADJ
fcis-15765	254	25	network[j	network[j	NOUN
fcis-15765	254	26	]	]	PUNCT
fcis-15765	254	27	.	.	PUNCT
fcis-15765	255	1	advances	advance	NOUN
fcis-15765	255	2	in	in	ADP
fcis-15765	255	3	neural	neural	ADJ
fcis-15765	255	4	information	information	NOUN
fcis-15765	255	5	processing	processing	NOUN
fcis-15765	255	6	systems	system	NOUN
fcis-15765	255	7	,	,	PUNCT
fcis-15765	255	8	2014	2014	NUM
fcis-15765	255	9	,	,	PUNCT
fcis-15765	255	10	27	27	NUM
fcis-15765	255	11	.	.	PUNCT
fcis-15765	256	1	[	[	X
fcis-15765	256	2	11	11	NUM
fcis-15765	256	3	]	]	X
fcis-15765	256	4	laina	laina	PROPN
fcis-15765	256	5	i	i	PROPN
fcis-15765	256	6	,	,	PUNCT
fcis-15765	256	7	rupprecht	rupprecht	PROPN
fcis-15765	256	8	c	c	AUX
fcis-15765	256	9	,	,	PUNCT
fcis-15765	256	10	belagiannis	belagiannis	NOUN
fcis-15765	256	11	v	v	NOUN
fcis-15765	256	12	,	,	PUNCT
fcis-15765	256	13	et	et	PROPN
fcis-15765	256	14	al	al	PROPN
fcis-15765	256	15	.	.	PUNCT
fcis-15765	257	1	deeper	deep	ADJ
fcis-15765	257	2	depth	depth	NOUN
fcis-15765	257	3	prediction	prediction	NOUN
fcis-15765	257	4	with	with	ADP
fcis-15765	257	5	fully	fully	ADV
fcis-15765	257	6	convolutional	convolutional	ADJ
fcis-15765	257	7	residual	residual	ADJ
fcis-15765	257	8	networks[c]//2016	networks[c]//2016	PROPN
fcis-15765	257	9	fourth	fourth	ADJ
fcis-15765	257	10	international	international	ADJ
fcis-15765	257	11	conference	conference	NOUN
fcis-15765	257	12	on	on	ADP
fcis-15765	257	13	3d	3d	PROPN
fcis-15765	257	14	vision	vision	NOUN
fcis-15765	257	15	(	(	PUNCT
fcis-15765	257	16	3dv	3dv	NOUN
fcis-15765	257	17	)	)	PUNCT
fcis-15765	257	18	.	.	PUNCT
fcis-15765	258	1	ieee	ieee	PROPN
fcis-15765	258	2	,	,	PUNCT
fcis-15765	258	3	2016	2016	NUM
fcis-15765	258	4	:	:	PUNCT
fcis-15765	258	5	239	239	NUM
fcis-15765	258	6	-	-	SYM
fcis-15765	258	7	248	248	NUM
fcis-15765	258	8	.	.	PUNCT
fcis-15765	259	1	[	[	X
fcis-15765	259	2	12	12	NUM
fcis-15765	259	3	]	]	X
fcis-15765	259	4	fu	fu	PROPN
fcis-15765	259	5	h	h	NOUN
fcis-15765	259	6	,	,	PUNCT
fcis-15765	259	7	gong	gong	PROPN
fcis-15765	259	8	m	m	PROPN
fcis-15765	259	9	,	,	PUNCT
fcis-15765	259	10	wang	wang	PROPN
fcis-15765	259	11	c	c	PROPN
fcis-15765	259	12	,	,	PUNCT
fcis-15765	259	13	et	et	PROPN
fcis-15765	259	14	al	al	PROPN
fcis-15765	259	15	.	.	PUNCT
fcis-15765	260	1	deep	deep	ADJ
fcis-15765	260	2	ordinal	ordinal	ADJ
fcis-15765	260	3	regression	regression	NOUN
fcis-15765	260	4	network	network	NOUN
fcis-15765	260	5	for	for	ADP
fcis-15765	260	6	monocular	monocular	ADJ
fcis-15765	260	7	depth	depth	NOUN
fcis-15765	260	8	estimation[c]//proceedings	estimation[c]//proceeding	NOUN
fcis-15765	260	9	of	of	ADP
fcis-15765	260	10	the	the	DET
fcis-15765	260	11	ieee	ieee	NOUN
fcis-15765	260	12	conference	conference	NOUN
fcis-15765	260	13	on	on	ADP
fcis-15765	260	14	computer	computer	NOUN
fcis-15765	260	15	vision	vision	NOUN
fcis-15765	260	16	and	and	CCONJ
fcis-15765	260	17	pattern	pattern	NOUN
fcis-15765	260	18	recognition	recognition	NOUN
fcis-15765	260	19	.	.	PUNCT
fcis-15765	261	1	2018	2018	NUM
fcis-15765	261	2	:	:	PUNCT
fcis-15765	261	3	2002	2002	NUM
fcis-15765	261	4	-	-	SYM
fcis-15765	261	5	2011	2011	NUM
fcis-15765	261	6	.	.	PUNCT
fcis-15765	262	1	[	[	X
fcis-15765	262	2	13	13	NUM
fcis-15765	262	3	]	]	X
fcis-15765	262	4	garg	garg	NOUN
fcis-15765	262	5	r	r	PROPN
fcis-15765	262	6	,	,	PUNCT
fcis-15765	262	7	bg	bg	PROPN
fcis-15765	262	8	v	v	NUM
fcis-15765	262	9	k	k	PROPN
fcis-15765	262	10	,	,	PUNCT
fcis-15765	262	11	carneiro	carneiro	PROPN
fcis-15765	262	12	g	g	PROPN
fcis-15765	262	13	,	,	PUNCT
fcis-15765	262	14	et	et	PROPN
fcis-15765	262	15	al	al	PROPN
fcis-15765	262	16	.	.	PROPN
fcis-15765	263	1	unsupervised	unsupervised	PROPN
fcis-15765	263	2	cnn	cnn	PROPN
fcis-15765	263	3	for	for	ADP
fcis-15765	263	4	single	single	ADJ
fcis-15765	263	5	view	view	NOUN
fcis-15765	263	6	depth	depth	NOUN
fcis-15765	263	7	estimation	estimation	NOUN
fcis-15765	263	8	:	:	PUNCT
fcis-15765	263	9	geometry	geometry	NOUN
fcis-15765	263	10	to	to	ADP
fcis-15765	263	11	the	the	DET
fcis-15765	263	12	rescue[c]//computer	rescue[c]//computer	PROPN
fcis-15765	263	13	vision	vision	PROPN
fcis-15765	263	14	–	–	PUNCT
fcis-15765	263	15	eccv	eccv	NOUN
fcis-15765	263	16	2016	2016	NUM
fcis-15765	263	17	:	:	PUNCT
fcis-15765	263	18	14th	14th	ADJ
fcis-15765	263	19	european	european	ADJ
fcis-15765	263	20	conference	conference	PROPN
fcis-15765	263	21	,	,	PUNCT
fcis-15765	263	22	amsterdam	amsterdam	PROPN
fcis-15765	263	23	,	,	PUNCT
fcis-15765	263	24	the	the	DET
fcis-15765	263	25	netherlands	netherlands	PROPN
fcis-15765	263	26	,	,	PUNCT
fcis-15765	263	27	october	october	PROPN
fcis-15765	263	28	11	11	NUM
fcis-15765	263	29	-	-	SYM
fcis-15765	263	30	14	14	NUM
fcis-15765	263	31	,	,	PUNCT
fcis-15765	263	32	2016	2016	NUM
fcis-15765	263	33	,	,	PUNCT
fcis-15765	263	34	proceedings	proceeding	NOUN
fcis-15765	263	35	,	,	PUNCT
fcis-15765	263	36	part	part	NOUN
fcis-15765	263	37	viii	viii	ADJ
fcis-15765	263	38	14	14	NUM
fcis-15765	263	39	.	.	PUNCT
fcis-15765	264	1	springer	springer	NOUN
fcis-15765	264	2	international	international	ADJ
fcis-15765	264	3	publishing	publishing	NOUN
fcis-15765	264	4	,	,	PUNCT
fcis-15765	264	5	2016	2016	NUM
fcis-15765	264	6	:	:	PUNCT
fcis-15765	265	1	740	740	NUM
fcis-15765	265	2	-	-	SYM
fcis-15765	265	3	756	756	NUM
fcis-15765	265	4	.	.	PUNCT
fcis-15765	266	1	[	[	X
fcis-15765	266	2	14	14	NUM
fcis-15765	266	3	]	]	X
fcis-15765	266	4	zhou	zhou	PROPN
fcis-15765	266	5	t	t	PROPN
fcis-15765	266	6	,	,	PUNCT
fcis-15765	266	7	brown	brown	PROPN
fcis-15765	266	8	m	m	PROPN
fcis-15765	266	9	,	,	PUNCT
fcis-15765	266	10	snavely	snavely	ADV
fcis-15765	266	11	n	n	CCONJ
fcis-15765	266	12	,	,	PUNCT
fcis-15765	266	13	et	et	PROPN
fcis-15765	266	14	al	al	PROPN
fcis-15765	266	15	.	.	PUNCT
fcis-15765	267	1	unsupervised	unsupervised	ADJ
fcis-15765	267	2	learning	learning	NOUN
fcis-15765	267	3	of	of	ADP
fcis-15765	267	4	depth	depth	NOUN
fcis-15765	267	5	and	and	CCONJ
fcis-15765	267	6	ego	ego	NOUN
fcis-15765	267	7	-	-	PUNCT
fcis-15765	267	8	motion	motion	NOUN
fcis-15765	267	9	from	from	ADP
fcis-15765	267	10	video[c]//proceedings	video[c]//proceeding	NOUN
fcis-15765	267	11	of	of	ADP
fcis-15765	267	12	the	the	DET
fcis-15765	267	13	ieee	ieee	NOUN
fcis-15765	267	14	conference	conference	NOUN
fcis-15765	267	15	on	on	ADP
fcis-15765	267	16	computer	computer	NOUN
fcis-15765	267	17	vision	vision	NOUN
fcis-15765	267	18	and	and	CCONJ
fcis-15765	267	19	pattern	pattern	NOUN
fcis-15765	267	20	recognition	recognition	NOUN
fcis-15765	267	21	.	.	PUNCT
fcis-15765	268	1	2017	2017	NUM
fcis-15765	268	2	:	:	PUNCT
fcis-15765	268	3	1851	1851	NUM
fcis-15765	268	4	-	-	SYM
fcis-15765	268	5	1858	1858	NUM
fcis-15765	268	6	.	.	PUNCT
fcis-15765	269	1	[	[	X
fcis-15765	269	2	15	15	NUM
fcis-15765	269	3	]	]	X
fcis-15765	269	4	ullman	ullman	PROPN
fcis-15765	269	5	s.	s.	PROPN
fcis-15765	269	6	the	the	DET
fcis-15765	269	7	interpretation	interpretation	NOUN
fcis-15765	269	8	of	of	ADP
fcis-15765	269	9	structure	structure	NOUN
fcis-15765	269	10	from	from	ADP
fcis-15765	269	11	motion[j	motion[j	PROPN
fcis-15765	269	12	]	]	PUNCT
fcis-15765	269	13	.	.	PUNCT
fcis-15765	270	1	proceedings	proceeding	NOUN
fcis-15765	270	2	of	of	ADP
fcis-15765	270	3	the	the	DET
fcis-15765	270	4	royal	royal	ADJ
fcis-15765	270	5	society	society	NOUN
fcis-15765	270	6	of	of	ADP
fcis-15765	270	7	london	london	PROPN
fcis-15765	270	8	.	.	PUNCT
fcis-15765	271	1	series	series	PROPN
fcis-15765	271	2	b.	b.	PROPN
fcis-15765	271	3	biological	biological	PROPN
fcis-15765	271	4	sciences	sciences	PROPN
fcis-15765	271	5	,	,	PUNCT
fcis-15765	271	6	1979	1979	NUM
fcis-15765	271	7	,	,	PUNCT
fcis-15765	271	8	203(1153	203(1153	NUM
fcis-15765	271	9	):	):	PUNCT
fcis-15765	271	10	405	405	NUM
fcis-15765	271	11	-	-	SYM
fcis-15765	271	12	426	426	NUM
fcis-15765	271	13	.	.	PUNCT
fcis-15765	272	1	[	[	X
fcis-15765	272	2	16	16	NUM
fcis-15765	272	3	]	]	X
fcis-15765	272	4	mancini	mancini	PROPN
fcis-15765	272	5	f	f	PROPN
fcis-15765	272	6	,	,	PUNCT
fcis-15765	272	7	dubbini	dubbini	PROPN
fcis-15765	272	8	m	m	PROPN
fcis-15765	272	9	,	,	PUNCT
fcis-15765	272	10	gattelli	gattelli	PROPN
fcis-15765	272	11	m	m	PROPN
fcis-15765	272	12	,	,	PUNCT
fcis-15765	272	13	et	et	PROPN
fcis-15765	272	14	al	al	PROPN
fcis-15765	272	15	.	.	PUNCT
fcis-15765	272	16	using	use	VERB
fcis-15765	272	17	unmanned	unmanned	ADJ
fcis-15765	272	18	aerial	aerial	ADJ
fcis-15765	272	19	vehicles	vehicle	NOUN
fcis-15765	272	20	(	(	PUNCT
fcis-15765	272	21	uav	uav	PROPN
fcis-15765	272	22	)	)	PUNCT
fcis-15765	272	23	for	for	ADP
fcis-15765	272	24	high	high	ADJ
fcis-15765	272	25	-	-	PUNCT
fcis-15765	272	26	resolution	resolution	NOUN
fcis-15765	272	27	reconstruction	reconstruction	NOUN
fcis-15765	272	28	of	of	ADP
fcis-15765	272	29	topography	topography	NOUN
fcis-15765	272	30	:	:	PUNCT
fcis-15765	272	31	the	the	DET
fcis-15765	272	32	structure	structure	NOUN
fcis-15765	272	33	from	from	ADP
fcis-15765	272	34	motion	motion	NOUN
fcis-15765	272	35	approach	approach	NOUN
fcis-15765	272	36	on	on	ADP
fcis-15765	272	37	coastal	coastal	ADJ
fcis-15765	272	38	environments[j	environments[j	PROPN
fcis-15765	272	39	]	]	PUNCT
fcis-15765	272	40	.	.	PUNCT
fcis-15765	273	1	remote	remote	ADJ
fcis-15765	273	2	sensing	sensing	NOUN
fcis-15765	273	3	,	,	PUNCT
fcis-15765	273	4	2013	2013	NUM
fcis-15765	273	5	,	,	PUNCT
fcis-15765	273	6	5(12	5(12	NUM
fcis-15765	273	7	):	):	PUNCT
fcis-15765	273	8	6880	6880	NUM
fcis-15765	273	9	-	-	SYM
fcis-15765	273	10	6898	6898	NUM
fcis-15765	273	11	.	.	PUNCT
fcis-15765	274	1	[	[	X
fcis-15765	274	2	17	17	NUM
fcis-15765	274	3	]	]	X
fcis-15765	274	4	mur	mur	PROPN
fcis-15765	274	5	-	-	PROPN
fcis-15765	274	6	artal	artal	ADJ
fcis-15765	274	7	r	r	PROPN
fcis-15765	274	8	,	,	PUNCT
fcis-15765	274	9	montiel	montiel	PROPN
fcis-15765	274	10	j	j	PROPN
fcis-15765	274	11	m	m	VERB
fcis-15765	274	12	m	m	PROPN
fcis-15765	274	13	,	,	PUNCT
fcis-15765	274	14	tardos	tardo	VERB
fcis-15765	274	15	j	j	PROPN
fcis-15765	274	16	d.	d.	PROPN
fcis-15765	274	17	orb	orb	PROPN
fcis-15765	274	18	-	-	PUNCT
fcis-15765	274	19	slam	slam	NOUN
fcis-15765	274	20	:	:	PUNCT
fcis-15765	274	21	a	a	DET
fcis-15765	274	22	versatile	versatile	ADJ
fcis-15765	274	23	and	and	CCONJ
fcis-15765	274	24	accurate	accurate	ADJ
fcis-15765	274	25	monocular	monocular	ADJ
fcis-15765	274	26	slam	slam	NOUN
fcis-15765	274	27	system[j	system[j	NOUN
fcis-15765	274	28	]	]	PUNCT
fcis-15765	274	29	.	.	PUNCT
fcis-15765	275	1	ieee	ieee	NOUN
fcis-15765	275	2	transactions	transaction	NOUN
fcis-15765	275	3	on	on	ADP
fcis-15765	275	4	robotics	robotic	NOUN
fcis-15765	275	5	,	,	PUNCT
fcis-15765	275	6	2015	2015	NUM
fcis-15765	275	7	,	,	PUNCT
fcis-15765	275	8	31(5	31(5	NUM
fcis-15765	275	9	):	):	PUNCT
fcis-15765	275	10	1147	1147	NUM
fcis-15765	275	11	-	-	SYM
fcis-15765	275	12	1163	1163	NUM
fcis-15765	275	13	.	.	PUNCT
fcis-15765	276	1	[	[	X
fcis-15765	276	2	18	18	NUM
fcis-15765	276	3	]	]	PUNCT
fcis-15765	276	4	szeliski	szeliski	PROPN
fcis-15765	276	5	r	r	NOUN
fcis-15765	276	6	,	,	PUNCT
fcis-15765	276	7	kang	kang	PROPN
fcis-15765	276	8	s	s	PROPN
fcis-15765	276	9	b.	b.	PROPN
fcis-15765	276	10	shape	shape	NOUN
fcis-15765	276	11	ambiguities	ambiguity	NOUN
fcis-15765	276	12	in	in	ADP
fcis-15765	276	13	structure	structure	NOUN
fcis-15765	276	14	from	from	ADP
fcis-15765	276	15	motion[j	motion[j	PROPN
fcis-15765	276	16	]	]	PUNCT
fcis-15765	276	17	.	.	PUNCT
fcis-15765	277	1	ieee	ieee	NOUN
fcis-15765	277	2	transactions	transaction	NOUN
fcis-15765	277	3	on	on	ADP
fcis-15765	277	4	pattern	pattern	NOUN
fcis-15765	277	5	analysis	analysis	NOUN
fcis-15765	277	6	and	and	CCONJ
fcis-15765	277	7	machine	machine	NOUN
fcis-15765	277	8	intelligence	intelligence	NOUN
fcis-15765	277	9	,	,	PUNCT
fcis-15765	277	10	1997	1997	NUM
fcis-15765	277	11	,	,	PUNCT
fcis-15765	277	12	19(5	19(5	NUM
fcis-15765	277	13	):	):	PUNCT
fcis-15765	277	14	506	506	NUM
fcis-15765	277	15	-	-	SYM
fcis-15765	277	16	512	512	NUM
fcis-15765	277	17	.	.	PUNCT
fcis-15765	278	1	[	[	X
fcis-15765	278	2	19	19	NUM
fcis-15765	278	3	]	]	PUNCT
fcis-15765	278	4	zou	zou	PROPN
fcis-15765	278	5	l	l	PROPN
fcis-15765	278	6	,	,	PUNCT
fcis-15765	278	7	li	li	PROPN
fcis-15765	278	8	y.	y.	PROPN
fcis-15765	278	9	a	a	DET
fcis-15765	278	10	method	method	NOUN
fcis-15765	278	11	of	of	ADP
fcis-15765	278	12	stereo	stereo	ADJ
fcis-15765	278	13	vision	vision	NOUN
fcis-15765	278	14	matching	matching	NOUN
fcis-15765	278	15	based	base	VERB
fcis-15765	278	16	on	on	ADP
fcis-15765	278	17	opencv[c]//2010	opencv[c]//2010	PROPN
fcis-15765	278	18	international	international	ADJ
fcis-15765	278	19	conference	conference	PROPN
fcis-15765	278	20	on	on	ADP
fcis-15765	278	21	audio	audio	NOUN
fcis-15765	278	22	,	,	PUNCT
fcis-15765	278	23	language	language	NOUN
fcis-15765	278	24	and	and	CCONJ
fcis-15765	278	25	image	image	NOUN
fcis-15765	278	26	processing	processing	NOUN
fcis-15765	278	27	.	.	PUNCT
fcis-15765	279	1	ieee	ieee	PROPN
fcis-15765	279	2	,	,	PUNCT
fcis-15765	279	3	2010	2010	NUM
fcis-15765	279	4	:	:	PUNCT
fcis-15765	279	5	185	185	NUM
fcis-15765	279	6	-	-	SYM
fcis-15765	279	7	190	190	NUM
fcis-15765	279	8	.	.	PUNCT
fcis-15765	280	1	[	[	X
fcis-15765	280	2	20	20	NUM
fcis-15765	280	3	]	]	X
fcis-15765	280	4	cao	cao	PROPN
fcis-15765	280	5	z	z	PROPN
fcis-15765	280	6	l	l	PROPN
fcis-15765	280	7	,	,	PUNCT
fcis-15765	280	8	yan	yan	PROPN
fcis-15765	280	9	z	z	PROPN
fcis-15765	280	10	h	h	PROPN
fcis-15765	280	11	,	,	PUNCT
fcis-15765	280	12	wang	wang	PROPN
fcis-15765	280	13	h.	h.	PROPN
fcis-15765	280	14	summary	summary	PROPN
fcis-15765	280	15	of	of	ADP
fcis-15765	280	16	binocular	binocular	ADJ
fcis-15765	280	17	stereo	stereo	NOUN
fcis-15765	280	18	vision	vision	NOUN
fcis-15765	280	19	matching	match	VERB
fcis-15765	280	20	technology[j	technology[j	PROPN
fcis-15765	280	21	]	]	PUNCT
fcis-15765	280	22	.	.	PUNCT
fcis-15765	281	1	journal	journal	PROPN
fcis-15765	281	2	of	of	ADP
fcis-15765	281	3	chongqing	chongqing	PROPN
fcis-15765	281	4	university	university	PROPN
fcis-15765	281	5	of	of	ADP
fcis-15765	281	6	technology	technology	NOUN
fcis-15765	281	7	(	(	PUNCT
fcis-15765	281	8	natural	natural	ADJ
fcis-15765	281	9	science	science	NOUN
fcis-15765	281	10	)	)	PUNCT
fcis-15765	281	11	,	,	PUNCT
fcis-15765	281	12	2015	2015	NUM
fcis-15765	281	13	,	,	PUNCT
fcis-15765	281	14	29(2	29(2	NUM
fcis-15765	281	15	):	):	PUNCT
fcis-15765	281	16	7075	7075	NUM
fcis-15765	281	17	.	.	PUNCT
fcis-15765	282	1	[	[	X
fcis-15765	282	2	21	21	NUM
fcis-15765	282	3	]	]	X
fcis-15765	282	4	benosman	benosman	NOUN
fcis-15765	282	5	r	r	NOUN
fcis-15765	282	6	,	,	PUNCT
fcis-15765	282	7	manière	manière	PROPN
fcis-15765	282	8	t	t	PROPN
fcis-15765	282	9	,	,	PUNCT
fcis-15765	282	10	devars	devar	VERB
fcis-15765	282	11	j.	j.	PROPN
fcis-15765	282	12	multidirectional	multidirectional	PROPN
fcis-15765	282	13	stereovision	stereovision	PROPN
fcis-15765	282	14	sensor	sensor	NOUN
fcis-15765	282	15	,	,	PUNCT
fcis-15765	282	16	calibration	calibration	NOUN
fcis-15765	282	17	and	and	CCONJ
fcis-15765	282	18	scenes	scene	VERB
fcis-15765	282	19	reconstruction	reconstruction	NOUN
fcis-15765	282	20	[	[	X
fcis-15765	282	21	c]//	c]//	ADJ
fcis-15765	282	22	proceedings	proceeding	NOUN
fcis-15765	282	23	of	of	ADP
fcis-15765	282	24	13th	13th	ADJ
fcis-15765	282	25	international	international	ADJ
fcis-15765	282	26	conference	conference	NOUN
fcis-15765	282	27	on	on	ADP
fcis-15765	282	28	pattern	pattern	NOUN
fcis-15765	282	29	recognition	recognition	NOUN
fcis-15765	282	30	.	.	PUNCT
fcis-15765	283	1	ieee	ieee	PROPN
fcis-15765	283	2	,	,	PUNCT
fcis-15765	283	3	1996	1996	NUM
fcis-15765	283	4	,	,	PUNCT
fcis-15765	283	5	1	1	NUM
fcis-15765	283	6	:	:	SYM
fcis-15765	283	7	161	161	NUM
fcis-15765	283	8	-	-	SYM
fcis-15765	283	9	165	165	NUM
fcis-15765	283	10	.	.	PUNCT
fcis-15765	284	1	[	[	X
fcis-15765	284	2	22	22	NUM
fcis-15765	284	3	]	]	X
fcis-15765	284	4	ramírez	ramírez	NOUN
fcis-15765	284	5	-	-	PUNCT
fcis-15765	284	6	hernández	hernández	NOUN
fcis-15765	284	7	l	l	NOUN
fcis-15765	284	8	r	r	PROPN
fcis-15765	284	9	,	,	PUNCT
fcis-15765	284	10	rodríguez	rodríguez	NOUN
fcis-15765	284	11	-	-	PUNCT
fcis-15765	284	12	quinoñez	quinoñez	NOUN
fcis-15765	284	13	j	j	PROPN
fcis-15765	284	14	c	c	PROPN
fcis-15765	284	15	,	,	PUNCT
fcis-15765	284	16	castrotoscano	castrotoscano	VERB
fcis-15765	284	17	m	m	PROPN
fcis-15765	284	18	j	j	PROPN
fcis-15765	284	19	,	,	PUNCT
fcis-15765	284	20	et	et	PROPN
fcis-15765	284	21	al	al	PROPN
fcis-15765	284	22	.	.	PROPN
fcis-15765	284	23	improve	improve	VERB
fcis-15765	284	24	three	three	NUM
fcis-15765	284	25	-	-	PUNCT
fcis-15765	284	26	dimensional	dimensional	ADJ
fcis-15765	284	27	point	point	NOUN
fcis-15765	284	28	localization	localization	NOUN
fcis-15765	284	29	accuracy	accuracy	NOUN
fcis-15765	284	30	in	in	ADP
fcis-15765	284	31	stereo	stereo	ADJ
fcis-15765	284	32	vision	vision	NOUN
fcis-15765	284	33	systems	system	NOUN
fcis-15765	284	34	using	use	VERB
fcis-15765	284	35	a	a	DET
fcis-15765	284	36	novel	novel	ADJ
fcis-15765	284	37	camera	camera	NOUN
fcis-15765	284	38	calibration	calibration	NOUN
fcis-15765	284	39	method[j	method[j	NOUN
fcis-15765	284	40	]	]	PUNCT
fcis-15765	284	41	.	.	PUNCT
fcis-15765	285	1	international	international	ADJ
fcis-15765	285	2	journal	journal	PROPN
fcis-15765	285	3	of	of	ADP
fcis-15765	285	4	advanced	advanced	ADJ
fcis-15765	285	5	robotic	robotic	ADJ
fcis-15765	285	6	systems	system	NOUN
fcis-15765	285	7	,	,	PUNCT
fcis-15765	285	8	2020	2020	NUM
fcis-15765	285	9	,	,	PUNCT
fcis-15765	285	10	17(1	17(1	NUM
fcis-15765	285	11	):	):	PUNCT
fcis-15765	285	12	1729881419896717	1729881419896717	NUM
fcis-15765	285	13	.	.	PUNCT
fcis-15765	286	1	[	[	X
fcis-15765	286	2	23	23	NUM
fcis-15765	286	3	]	]	PUNCT
fcis-15765	286	4	tateno	tateno	NOUN
fcis-15765	286	5	k	k	PROPN
fcis-15765	286	6	,	,	PUNCT
fcis-15765	286	7	tombari	tombari	PROPN
fcis-15765	286	8	f	f	PROPN
fcis-15765	286	9	,	,	PUNCT
fcis-15765	286	10	laina	laina	PROPN
fcis-15765	286	11	i	i	PRON
fcis-15765	286	12	,	,	PUNCT
fcis-15765	286	13	et	et	PROPN
fcis-15765	286	14	al	al	PROPN
fcis-15765	286	15	.	.	PROPN
fcis-15765	287	1	cnn	cnn	PROPN
fcis-15765	287	2	-	-	PUNCT
fcis-15765	287	3	slam	slam	PROPN
fcis-15765	287	4	:	:	PUNCT
fcis-15765	287	5	real	real	ADJ
fcis-15765	287	6	-	-	PUNCT
fcis-15765	287	7	time	time	NOUN
fcis-15765	287	8	dense	dense	ADJ
fcis-15765	287	9	monocular	monocular	ADJ
fcis-15765	287	10	slam	slam	NOUN
fcis-15765	287	11	with	with	ADP
fcis-15765	287	12	learned	learn	VERB
fcis-15765	287	13	depth	depth	NOUN
fcis-15765	287	14	prediction[c]//proceedings	prediction[c]//proceeding	NOUN
fcis-15765	287	15	of	of	ADP
fcis-15765	287	16	the	the	DET
fcis-15765	287	17	ieee	ieee	NOUN
fcis-15765	287	18	conference	conference	NOUN
fcis-15765	287	19	on	on	ADP
fcis-15765	287	20	computer	computer	NOUN
fcis-15765	287	21	vision	vision	NOUN
fcis-15765	287	22	and	and	CCONJ
fcis-15765	287	23	pattern	pattern	NOUN
fcis-15765	287	24	recognition	recognition	NOUN
fcis-15765	287	25	.	.	PUNCT
fcis-15765	288	1	2017	2017	NUM
fcis-15765	288	2	:	:	PUNCT
fcis-15765	288	3	6243	6243	NUM
fcis-15765	288	4	-	-	SYM
fcis-15765	288	5	6252	6252	NUM
fcis-15765	288	6	.	.	PUNCT
fcis-15765	289	1	[	[	X
fcis-15765	289	2	24	24	NUM
fcis-15765	289	3	]	]	PUNCT
fcis-15765	289	4	yoneda	yoneda	PROPN
fcis-15765	289	5	k	k	PROPN
fcis-15765	289	6	,	,	PUNCT
fcis-15765	289	7	tehrani	tehrani	PROPN
fcis-15765	289	8	h	h	PROPN
fcis-15765	289	9	,	,	PUNCT
fcis-15765	289	10	ogawa	ogawa	PROPN
fcis-15765	289	11	t	t	PROPN
fcis-15765	289	12	,	,	PUNCT
fcis-15765	289	13	et	et	PROPN
fcis-15765	289	14	al	al	PROPN
fcis-15765	289	15	.	.	PROPN
fcis-15765	289	16	lidar	lidar	PROPN
fcis-15765	289	17	scan	scan	PROPN
fcis-15765	289	18	feature	feature	NOUN
fcis-15765	289	19	for	for	ADP
fcis-15765	289	20	localization	localization	NOUN
fcis-15765	289	21	with	with	ADP
fcis-15765	289	22	highly	highly	ADV
fcis-15765	289	23	precise	precise	ADJ
fcis-15765	289	24	3	3	NUM
fcis-15765	289	25	-	-	SYM
fcis-15765	289	26	d	d	NOUN
fcis-15765	289	27	map[c]//2014	map[c]//2014	NOUN
fcis-15765	289	28	ieee	ieee	NOUN
fcis-15765	289	29	intelligent	intelligent	ADJ
fcis-15765	289	30	vehicles	vehicle	NOUN
fcis-15765	289	31	symposium	symposium	NOUN
fcis-15765	289	32	proceedings	proceeding	NOUN
fcis-15765	289	33	.	.	PUNCT
fcis-15765	290	1	ieee	ieee	NOUN
fcis-15765	290	2	,	,	PUNCT
fcis-15765	290	3	2014	2014	NUM
fcis-15765	290	4	:	:	PUNCT
fcis-15765	290	5	1345	1345	NUM
fcis-15765	290	6	-	-	SYM
fcis-15765	290	7	1350	1350	NUM
fcis-15765	290	8	.	.	PUNCT
fcis-15765	291	1	[	[	X
fcis-15765	291	2	25	25	NUM
fcis-15765	291	3	]	]	X
fcis-15765	291	4	silberman	silberman	NOUN
fcis-15765	291	5	n	n	CCONJ
fcis-15765	291	6	,	,	PUNCT
fcis-15765	291	7	hoiem	hoiem	ADJ
fcis-15765	291	8	d	d	NOUN
fcis-15765	291	9	,	,	PUNCT
fcis-15765	291	10	kohli	kohli	PROPN
fcis-15765	291	11	p	p	X
fcis-15765	291	12	,	,	PUNCT
fcis-15765	291	13	et	et	PROPN
fcis-15765	291	14	al	al	PROPN
fcis-15765	291	15	.	.	PROPN
fcis-15765	291	16	indoor	indoor	ADJ
fcis-15765	291	17	segmentation	segmentation	NOUN
fcis-15765	291	18	and	and	CCONJ
fcis-15765	291	19	support	support	NOUN
fcis-15765	291	20	inference	inference	NOUN
fcis-15765	291	21	from	from	ADP
fcis-15765	291	22	rgbd	rgbd	NOUN
fcis-15765	291	23	images[c]//computer	images[c]//computer	PROPN
fcis-15765	291	24	vision	vision	NOUN
fcis-15765	291	25	–	–	PUNCT
fcis-15765	291	26	eccv	eccv	ADJ
fcis-15765	291	27	2012	2012	NUM
fcis-15765	291	28	:	:	PUNCT
fcis-15765	291	29	12th	12th	ADJ
fcis-15765	291	30	european	european	PROPN
fcis-15765	291	31	conference	conference	NOUN
fcis-15765	291	32	on	on	ADP
fcis-15765	291	33	computer	computer	NOUN
fcis-15765	291	34	vision	vision	NOUN
fcis-15765	291	35	,	,	PUNCT
fcis-15765	291	36	florence	florence	NOUN
fcis-15765	291	37	,	,	PUNCT
fcis-15765	291	38	italy	italy	PROPN
fcis-15765	291	39	,	,	PUNCT
fcis-15765	291	40	october	october	PROPN
fcis-15765	291	41	7	7	NUM
fcis-15765	291	42	-	-	SYM
fcis-15765	291	43	13	13	NUM
fcis-15765	291	44	,	,	PUNCT
fcis-15765	291	45	2012	2012	NUM
fcis-15765	291	46	,	,	PUNCT
fcis-15765	291	47	proceedings	proceeding	NOUN
fcis-15765	291	48	,	,	PUNCT
fcis-15765	291	49	part	part	NOUN
fcis-15765	291	50	v	v	ADP
fcis-15765	291	51	12	12	NUM
fcis-15765	291	52	.	.	PUNCT
fcis-15765	292	1	springer	springer	PROPN
fcis-15765	292	2	berlin	berlin	PROPN
fcis-15765	292	3	heidelberg	heidelberg	PROPN
fcis-15765	292	4	,	,	PUNCT
fcis-15765	292	5	2012	2012	NUM
fcis-15765	292	6	:	:	PUNCT
fcis-15765	292	7	746	746	NUM
fcis-15765	292	8	-	-	SYM
fcis-15765	292	9	760	760	NUM
fcis-15765	292	10	..	..	PUNCT
fcis-15765	293	1	[	[	X
fcis-15765	293	2	26	26	NUM
fcis-15765	293	3	]	]	X
fcis-15765	293	4	liu	liu	PROPN
fcis-15765	293	5	f	f	PROPN
fcis-15765	293	6	,	,	PUNCT
fcis-15765	293	7	shen	shen	PROPN
fcis-15765	293	8	c	c	X
fcis-15765	293	9	,	,	PUNCT
fcis-15765	293	10	lin	lin	PROPN
fcis-15765	293	11	g	g	PROPN
fcis-15765	293	12	,	,	PUNCT
fcis-15765	293	13	et	et	PROPN
fcis-15765	293	14	al	al	PROPN
fcis-15765	293	15	.	.	PUNCT
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fcis-15765	293	17	depth	depth	NOUN
fcis-15765	293	18	from	from	ADP
fcis-15765	293	19	single	single	ADJ
fcis-15765	293	20	monocular	monocular	ADJ
fcis-15765	293	21	images	image	NOUN
fcis-15765	293	22	using	use	VERB
fcis-15765	293	23	deep	deep	ADJ
fcis-15765	293	24	convolutional	convolutional	ADJ
fcis-15765	293	25	neural	neural	ADJ
fcis-15765	293	26	fields[j	fields[j	PROPN
fcis-15765	293	27	]	]	PUNCT
fcis-15765	293	28	.	.	PUNCT
fcis-15765	294	1	ieee	ieee	NOUN
fcis-15765	294	2	transactions	transaction	NOUN
fcis-15765	294	3	on	on	ADP
fcis-15765	294	4	pattern	pattern	NOUN
fcis-15765	294	5	analysis	analysis	NOUN
fcis-15765	294	6	and	and	CCONJ
fcis-15765	294	7	machine	machine	NOUN
fcis-15765	294	8	intelligence	intelligence	NOUN
fcis-15765	294	9	,	,	PUNCT
fcis-15765	294	10	2015	2015	NUM
fcis-15765	294	11	,	,	PUNCT
fcis-15765	294	12	38(10	38(10	NUM
fcis-15765	294	13	):	):	PUNCT
fcis-15765	294	14	2024	2024	NUM
fcis-15765	294	15	-	-	SYM
fcis-15765	294	16	2039	2039	NUM
fcis-15765	294	17	.	.	PUNCT
fcis-15765	295	1	[	[	X
fcis-15765	295	2	27	27	NUM
fcis-15765	295	3	]	]	X
fcis-15765	295	4	geiger	geiger	PROPN
fcis-15765	295	5	a	a	PROPN
fcis-15765	295	6	,	,	PUNCT
fcis-15765	295	7	lenz	lenz	PROPN
fcis-15765	295	8	p	p	X
fcis-15765	295	9	,	,	PUNCT
fcis-15765	295	10	stiller	stiller	NOUN
fcis-15765	295	11	c	c	PROPN
fcis-15765	295	12	,	,	PUNCT
fcis-15765	295	13	et	et	PROPN
fcis-15765	295	14	al	al	PROPN
fcis-15765	295	15	.	.	PROPN
fcis-15765	296	1	vision	vision	PROPN
fcis-15765	296	2	meets	meet	VERB
fcis-15765	296	3	robotics	robotic	NOUN
fcis-15765	296	4	:	:	PUNCT
fcis-15765	296	5	the	the	DET
fcis-15765	296	6	kitti	kitti	PROPN
fcis-15765	296	7	dataset[j	dataset[j	X
fcis-15765	296	8	]	]	PUNCT
fcis-15765	296	9	.	.	PUNCT
fcis-15765	297	1	the	the	DET
fcis-15765	297	2	international	international	ADJ
fcis-15765	297	3	journal	journal	NOUN
fcis-15765	297	4	of	of	ADP
fcis-15765	297	5	robotics	robotic	NOUN
fcis-15765	297	6	research	research	NOUN
fcis-15765	297	7	,	,	PUNCT
fcis-15765	297	8	2013	2013	NUM
fcis-15765	297	9	,	,	PUNCT
fcis-15765	297	10	32(11	32(11	NUM
fcis-15765	297	11	):	):	PUNCT
fcis-15765	297	12	1231	1231	NUM
fcis-15765	297	13	-	-	SYM
fcis-15765	297	14	1237	1237	NUM
fcis-15765	297	15	.	.	PUNCT
fcis-15765	298	1	[	[	X
fcis-15765	298	2	28	28	NUM
fcis-15765	298	3	]	]	PUNCT
fcis-15765	298	4	xie	xie	PROPN
fcis-15765	298	5	j	j	PROPN
fcis-15765	298	6	,	,	PUNCT
fcis-15765	298	7	kiefel	kiefel	VERB
fcis-15765	298	8	m	m	PROPN
fcis-15765	298	9	,	,	PUNCT
fcis-15765	298	10	sun	sun	PROPN
fcis-15765	298	11	m	m	PROPN
fcis-15765	298	12	t	t	PROPN
fcis-15765	298	13	,	,	PUNCT
fcis-15765	298	14	et	et	PROPN
fcis-15765	298	15	al	al	PROPN
fcis-15765	298	16	.	.	PROPN
fcis-15765	298	17	semantic	semantic	PROPN
fcis-15765	298	18	instance	instance	NOUN
fcis-15765	298	19	annotation	annotation	NOUN
fcis-15765	298	20	of	of	ADP
fcis-15765	298	21	street	street	NOUN
fcis-15765	298	22	scenes	scene	NOUN
fcis-15765	298	23	by	by	ADP
fcis-15765	298	24	3d	3d	NOUN
fcis-15765	298	25	to	to	ADP
fcis-15765	298	26	2d	2d	NUM
fcis-15765	298	27	label	label	NOUN
fcis-15765	298	28	transfer[c]//proceedings	transfer[c]//proceeding	NOUN
fcis-15765	298	29	of	of	ADP
fcis-15765	298	30	the	the	DET
fcis-15765	298	31	ieee	ieee	NOUN
fcis-15765	298	32	conference	conference	NOUN
fcis-15765	298	33	on	on	ADP
fcis-15765	298	34	computer	computer	NOUN
fcis-15765	298	35	vision	vision	NOUN
fcis-15765	298	36	and	and	CCONJ
fcis-15765	298	37	pattern	pattern	NOUN
fcis-15765	298	38	recognition	recognition	NOUN
fcis-15765	298	39	.	.	PUNCT
fcis-15765	299	1	2016	2016	NUM
fcis-15765	299	2	:	:	PUNCT
fcis-15765	299	3	3688	3688	NUM
fcis-15765	299	4	-	-	SYM
fcis-15765	299	5	3697	3697	NUM
fcis-15765	299	6	.	.	PUNCT
fcis-15765	300	1	[	[	X
fcis-15765	300	2	29	29	NUM
fcis-15765	300	3	]	]	X
fcis-15765	300	4	engel	engel	PROPN
fcis-15765	300	5	j	j	PROPN
fcis-15765	300	6	,	,	PUNCT
fcis-15765	300	7	schöps	schöps	PROPN
fcis-15765	300	8	t	t	PROPN
fcis-15765	300	9	,	,	PUNCT
fcis-15765	300	10	cremers	cremers	PROPN
fcis-15765	300	11	d.	d.	PROPN
fcis-15765	300	12	lsd	lsd	PROPN
fcis-15765	300	13	-	-	PUNCT
fcis-15765	300	14	slam	slam	NOUN
fcis-15765	300	15	:	:	PUNCT
fcis-15765	300	16	large	large	ADJ
fcis-15765	300	17	-	-	PUNCT
fcis-15765	300	18	scale	scale	NOUN
fcis-15765	300	19	direct	direct	ADJ
fcis-15765	300	20	monocular	monocular	ADJ
fcis-15765	300	21	slam[c]//european	slam[c]//european	ADJ
fcis-15765	300	22	conference	conference	NOUN
fcis-15765	300	23	on	on	ADP
fcis-15765	300	24	computer	computer	NOUN
fcis-15765	300	25	vision	vision	NOUN
fcis-15765	300	26	.	.	PUNCT
fcis-15765	301	1	cham	cham	PROPN
fcis-15765	301	2	:	:	PUNCT
fcis-15765	301	3	springer	springer	NOUN
fcis-15765	301	4	international	international	ADJ
fcis-15765	301	5	publishing	publishing	NOUN
fcis-15765	301	6	,	,	PUNCT
fcis-15765	301	7	2014	2014	NUM
fcis-15765	301	8	:	:	PUNCT
fcis-15765	301	9	834849	834849	NUM
fcis-15765	301	10	.	.	PUNCT
fcis-15765	302	1	[	[	X
fcis-15765	302	2	30	30	NUM
fcis-15765	302	3	]	]	X
fcis-15765	302	4	kendall	kendall	PROPN
fcis-15765	302	5	a	a	PROPN
fcis-15765	302	6	,	,	PUNCT
fcis-15765	302	7	cipolla	cipolla	PROPN
fcis-15765	302	8	r.	r.	PROPN
fcis-15765	302	9	geometric	geometric	PROPN
fcis-15765	302	10	loss	loss	NOUN
fcis-15765	302	11	functions	function	NOUN
fcis-15765	302	12	for	for	ADP
fcis-15765	302	13	camera	camera	NOUN
fcis-15765	302	14	pose	pose	NOUN
fcis-15765	302	15	regression	regression	NOUN
fcis-15765	302	16	with	with	ADP
fcis-15765	302	17	deep	deep	ADJ
fcis-15765	302	18	learning[c]//proceedings	learning[c]//proceeding	NOUN
fcis-15765	302	19	of	of	ADP
fcis-15765	302	20	the	the	DET
fcis-15765	302	21	ieee	ieee	NOUN
fcis-15765	302	22	conference	conference	NOUN
fcis-15765	302	23	on	on	ADP
fcis-15765	302	24	computer	computer	NOUN
fcis-15765	302	25	vision	vision	NOUN
fcis-15765	302	26	and	and	CCONJ
fcis-15765	302	27	pattern	pattern	NOUN
fcis-15765	302	28	recognition	recognition	NOUN
fcis-15765	302	29	.	.	PUNCT
fcis-15765	303	1	2017	2017	NUM
fcis-15765	303	2	:	:	PUNCT
fcis-15765	303	3	5974	5974	NUM
fcis-15765	303	4	-	-	SYM
fcis-15765	303	5	5983	5983	NUM
fcis-15765	303	6	.	.	PUNCT
fcis-15765	304	1	33	33	NUM
fcis-15765	305	1	[	[	SYM
fcis-15765	305	2	31	31	NUM
fcis-15765	305	3	]	]	X
fcis-15765	305	4	saxena	saxena	PROPN
fcis-15765	305	5	a	a	PROPN
fcis-15765	305	6	,	,	PUNCT
fcis-15765	305	7	sun	sun	PROPN
fcis-15765	305	8	m	m	PROPN
fcis-15765	305	9	,	,	PUNCT
fcis-15765	305	10	ng	ng	PROPN
fcis-15765	305	11	a	a	X
fcis-15765	305	12	y.	y.	PROPN
fcis-15765	305	13	make3d	make3d	PRON
fcis-15765	305	14	:	:	PUNCT
fcis-15765	305	15	learning	learn	VERB
fcis-15765	305	16	3d	3d	NUM
fcis-15765	305	17	scene	scene	NOUN
fcis-15765	305	18	structure	structure	NOUN
fcis-15765	305	19	from	from	ADP
fcis-15765	305	20	a	a	DET
fcis-15765	305	21	single	single	ADJ
fcis-15765	305	22	still	still	ADV
fcis-15765	305	23	image[j	image[j	ADJ
fcis-15765	305	24	]	]	PUNCT
fcis-15765	305	25	.	.	PUNCT
fcis-15765	306	1	ieee	ieee	NOUN
fcis-15765	306	2	transactions	transaction	NOUN
fcis-15765	306	3	on	on	ADP
fcis-15765	306	4	pattern	pattern	NOUN
fcis-15765	306	5	analysis	analysis	NOUN
fcis-15765	306	6	and	and	CCONJ
fcis-15765	306	7	machine	machine	NOUN
fcis-15765	306	8	intelligence	intelligence	NOUN
fcis-15765	306	9	,	,	PUNCT
fcis-15765	306	10	2008	2008	NUM
fcis-15765	306	11	,	,	PUNCT
fcis-15765	306	12	31(5	31(5	NUM
fcis-15765	306	13	):	):	PUNCT
fcis-15765	306	14	824840	824840	NUM
fcis-15765	306	15	.	.	PUNCT
fcis-15765	307	1	[	[	X
fcis-15765	307	2	32	32	NUM
fcis-15765	307	3	]	]	X
fcis-15765	307	4	godard	godard	PROPN
fcis-15765	307	5	c	c	PROPN
fcis-15765	307	6	,	,	PUNCT
fcis-15765	307	7	mac	mac	PROPN
fcis-15765	307	8	aodha	aodha	PROPN
fcis-15765	307	9	o	o	NOUN
fcis-15765	307	10	,	,	PUNCT
fcis-15765	307	11	brostow	brostow	PROPN
fcis-15765	307	12	g	g	PROPN
fcis-15765	307	13	j.	j.	PROPN
fcis-15765	307	14	unsupervised	unsupervised	ADJ
fcis-15765	307	15	monocular	monocular	ADJ
fcis-15765	307	16	depth	depth	NOUN
fcis-15765	307	17	estimation	estimation	NOUN
fcis-15765	307	18	with	with	ADP
fcis-15765	307	19	left	left	ADJ
fcis-15765	307	20	-	-	PUNCT
fcis-15765	307	21	right	right	NOUN
fcis-15765	307	22	consistency	consistency	NOUN
fcis-15765	307	23	[	[	X
fcis-15765	307	24	c]//	c]//	ADJ
fcis-15765	307	25	proceedings	proceeding	NOUN
fcis-15765	307	26	of	of	ADP
fcis-15765	307	27	the	the	DET
fcis-15765	307	28	ieee	ieee	NOUN
fcis-15765	307	29	conference	conference	NOUN
fcis-15765	307	30	on	on	ADP
fcis-15765	307	31	computer	computer	NOUN
fcis-15765	307	32	vision	vision	NOUN
fcis-15765	307	33	and	and	CCONJ
fcis-15765	307	34	pattern	pattern	NOUN
fcis-15765	307	35	recognition	recognition	NOUN
fcis-15765	307	36	.	.	PUNCT
fcis-15765	308	1	2017	2017	NUM
fcis-15765	308	2	:	:	PUNCT
fcis-15765	308	3	270	270	NUM
fcis-15765	308	4	-	-	SYM
fcis-15765	308	5	279	279	NUM
fcis-15765	308	6	.	.	PUNCT
fcis-15765	309	1	9	9	NUM
fcis-15765	310	1	[	[	SYM
fcis-15765	310	2	33	33	NUM
fcis-15765	310	3	]	]	PUNCT
fcis-15765	310	4	cordts	cordts	PROPN
fcis-15765	310	5	m	m	PROPN
fcis-15765	310	6	,	,	PUNCT
fcis-15765	310	7	omran	omran	PROPN
fcis-15765	310	8	m	m	PROPN
fcis-15765	310	9	,	,	PUNCT
fcis-15765	310	10	ramos	ramos	PROPN
fcis-15765	310	11	s	s	PROPN
fcis-15765	310	12	,	,	PUNCT
fcis-15765	310	13	et	et	PROPN
fcis-15765	310	14	al	al	PROPN
fcis-15765	310	15	.	.	PUNCT
fcis-15765	311	1	the	the	DET
fcis-15765	311	2	cityscapes	cityscape	NOUN
fcis-15765	311	3	dataset	dataset	VERB
fcis-15765	311	4	for	for	ADP
fcis-15765	311	5	semantic	semantic	ADJ
fcis-15765	311	6	urban	urban	ADJ
fcis-15765	311	7	scene	scene	NOUN
fcis-15765	311	8	understanding[c]//proceedings	understanding[c]//proceeding	NOUN
fcis-15765	311	9	of	of	ADP
fcis-15765	311	10	the	the	DET
fcis-15765	311	11	ieee	ieee	NOUN
fcis-15765	311	12	conference	conference	NOUN
fcis-15765	311	13	on	on	ADP
fcis-15765	311	14	computer	computer	NOUN
fcis-15765	311	15	vision	vision	NOUN
fcis-15765	311	16	and	and	CCONJ
fcis-15765	311	17	pattern	pattern	NOUN
fcis-15765	311	18	recognition	recognition	NOUN
fcis-15765	311	19	.	.	PUNCT
fcis-15765	312	1	2016	2016	NUM
fcis-15765	312	2	:	:	PUNCT
fcis-15765	312	3	3213	3213	NUM
fcis-15765	312	4	-	-	SYM
fcis-15765	312	5	3223	3223	NUM
fcis-15765	312	6	.	.	PUNCT
fcis-15765	313	1	[	[	X
fcis-15765	313	2	34	34	NUM
fcis-15765	313	3	]	]	PUNCT
fcis-15765	313	4	dos	do	NOUN
fcis-15765	313	5	santos	santos	PROPN
fcis-15765	313	6	rosa	rosa	PROPN
fcis-15765	313	7	n	n	PROPN
fcis-15765	313	8	,	,	PUNCT
fcis-15765	313	9	guizilini	guizilini	PROPN
fcis-15765	313	10	v	v	NOUN
fcis-15765	313	11	,	,	PUNCT
fcis-15765	313	12	grassi	grassi	PROPN
fcis-15765	313	13	v.	v.	ADP
fcis-15765	313	14	sparse	sparse	ADV
fcis-15765	313	15	-	-	PUNCT
fcis-15765	313	16	tocontinuous	tocontinuous	ADJ
fcis-15765	313	17	:	:	PUNCT
fcis-15765	313	18	enhancing	enhance	VERB
fcis-15765	313	19	monocular	monocular	ADJ
fcis-15765	313	20	depth	depth	NOUN
fcis-15765	313	21	estimation	estimation	NOUN
fcis-15765	313	22	using	use	VERB
fcis-15765	313	23	occupancy	occupancy	NOUN
fcis-15765	313	24	maps[c]//2019	maps[c]//2019	X
fcis-15765	313	25	19th	19th	ADJ
fcis-15765	313	26	international	international	ADJ
fcis-15765	313	27	conference	conference	NOUN
fcis-15765	313	28	on	on	ADP
fcis-15765	313	29	advanced	advanced	ADJ
fcis-15765	313	30	robotics	robotic	NOUN
fcis-15765	313	31	(	(	PUNCT
fcis-15765	313	32	icar	icar	ADJ
fcis-15765	313	33	)	)	PUNCT
fcis-15765	313	34	.	.	PUNCT
fcis-15765	314	1	ieee	ieee	PROPN
fcis-15765	314	2	,	,	PUNCT
fcis-15765	314	3	2019	2019	NUM
fcis-15765	314	4	:	:	PUNCT
fcis-15765	314	5	793	793	NUM
fcis-15765	314	6	-	-	SYM
fcis-15765	314	7	800	800	NUM
fcis-15765	314	8	.	.	PUNCT
fcis-15765	315	1	[	[	X
fcis-15765	315	2	35	35	NUM
fcis-15765	315	3	]	]	X
fcis-15765	315	4	ramos	ramos	PROPN
fcis-15765	315	5	f	f	PROPN
fcis-15765	315	6	,	,	PUNCT
fcis-15765	315	7	ott	ott	PROPN
fcis-15765	315	8	l.	l.	PROPN
fcis-15765	315	9	hilbert	hilbert	PROPN
fcis-15765	315	10	maps	maps	PROPN
fcis-15765	315	11	:	:	PUNCT
fcis-15765	315	12	scalable	scalable	ADJ
fcis-15765	315	13	continuous	continuous	ADJ
fcis-15765	315	14	occupancy	occupancy	NOUN
fcis-15765	315	15	mapping	mapping	NOUN
fcis-15765	315	16	with	with	ADP
fcis-15765	315	17	stochastic	stochastic	ADJ
fcis-15765	315	18	gradient	gradient	NOUN
fcis-15765	315	19	descent[j	descent[j	ADV
fcis-15765	315	20	]	]	PUNCT
fcis-15765	315	21	.	.	PUNCT
fcis-15765	316	1	the	the	DET
fcis-15765	316	2	international	international	ADJ
fcis-15765	316	3	journal	journal	NOUN
fcis-15765	316	4	of	of	ADP
fcis-15765	316	5	robotics	robotic	NOUN
fcis-15765	316	6	research	research	NOUN
fcis-15765	316	7	,	,	PUNCT
fcis-15765	316	8	2016	2016	NUM
fcis-15765	316	9	,	,	PUNCT
fcis-15765	316	10	35(14	35(14	NUM
fcis-15765	316	11	):	):	PUNCT
fcis-15765	316	12	1717	1717	NUM
fcis-15765	316	13	-	-	SYM
fcis-15765	316	14	1730	1730	NUM
fcis-15765	316	15	.	.	PUNCT
fcis-15765	317	1	[	[	X
fcis-15765	317	2	36	36	NUM
fcis-15765	317	3	]	]	X
fcis-15765	317	4	li	li	PROPN
fcis-15765	317	5	b	b	PROPN
fcis-15765	317	6	,	,	PUNCT
fcis-15765	317	7	dai	dai	PROPN
fcis-15765	317	8	y	y	PROPN
fcis-15765	317	9	,	,	PUNCT
fcis-15765	317	10	he	he	PRON
fcis-15765	317	11	m.	m.	VERB
fcis-15765	317	12	monocular	monocular	ADJ
fcis-15765	317	13	depth	depth	NOUN
fcis-15765	317	14	estimation	estimation	NOUN
fcis-15765	317	15	with	with	ADP
fcis-15765	317	16	hierarchical	hierarchical	ADJ
fcis-15765	317	17	fusion	fusion	NOUN
fcis-15765	317	18	of	of	ADP
fcis-15765	317	19	dilated	dilated	ADJ
fcis-15765	317	20	cnns	cnn	NOUN
fcis-15765	317	21	and	and	CCONJ
fcis-15765	317	22	soft	soft	ADJ
fcis-15765	317	23	-	-	PUNCT
fcis-15765	317	24	weighted	weight	VERB
fcis-15765	317	25	-	-	PUNCT
fcis-15765	317	26	sum	sum	NOUN
fcis-15765	317	27	inference[j	inference[j	NOUN
fcis-15765	317	28	]	]	PUNCT
fcis-15765	317	29	.	.	PUNCT
fcis-15765	318	1	pattern	pattern	NOUN
fcis-15765	318	2	recognition	recognition	NOUN
fcis-15765	318	3	,	,	PUNCT
fcis-15765	318	4	2018	2018	NUM
fcis-15765	318	5	,	,	PUNCT
fcis-15765	318	6	83	83	NUM
fcis-15765	318	7	:	:	SYM
fcis-15765	318	8	328	328	NUM
fcis-15765	318	9	-	-	SYM
fcis-15765	318	10	339	339	NUM
fcis-15765	318	11	.	.	PUNCT
fcis-15765	319	1	[	[	X
fcis-15765	319	2	37	37	NUM
fcis-15765	319	3	]	]	PUNCT
fcis-15765	319	4	zou	zou	PROPN
fcis-15765	319	5	h	h	PROPN
fcis-15765	319	6	,	,	PUNCT
fcis-15765	319	7	xian	xian	PROPN
fcis-15765	319	8	k	k	PROPN
fcis-15765	319	9	,	,	PUNCT
fcis-15765	319	10	yang	yang	PROPN
fcis-15765	319	11	j	j	PROPN
fcis-15765	319	12	,	,	PUNCT
fcis-15765	319	13	et	et	PROPN
fcis-15765	319	14	al	al	PROPN
fcis-15765	319	15	.	.	PUNCT
fcis-15765	320	1	mean	mean	ADJ
fcis-15765	320	2	-	-	PUNCT
fcis-15765	320	3	variance	variance	NOUN
fcis-15765	320	4	loss	loss	NOUN
fcis-15765	320	5	for	for	ADP
fcis-15765	320	6	monocular	monocular	ADJ
fcis-15765	320	7	depth	depth	NOUN
fcis-15765	320	8	estimation[c]//2019	estimation[c]//2019	NUM
fcis-15765	320	9	ieee	ieee	NOUN
fcis-15765	320	10	international	international	ADJ
fcis-15765	320	11	conference	conference	NOUN
fcis-15765	320	12	on	on	ADP
fcis-15765	320	13	image	image	NOUN
fcis-15765	320	14	processing	processing	NOUN
fcis-15765	320	15	(	(	PUNCT
fcis-15765	320	16	icip	icip	PROPN
fcis-15765	320	17	)	)	PUNCT
fcis-15765	320	18	.	.	PUNCT
fcis-15765	321	1	ieee	ieee	PROPN
fcis-15765	321	2	,	,	PUNCT
fcis-15765	321	3	2019	2019	NUM
fcis-15765	321	4	:	:	SYM
fcis-15765	321	5	17601764	17601764	NUM
fcis-15765	321	6	.	.	PUNCT
fcis-15765	322	1	[	[	X
fcis-15765	322	2	38	38	NUM
fcis-15765	322	3	]	]	X
fcis-15765	322	4	eigen	eigen	PROPN
fcis-15765	322	5	d	d	PROPN
fcis-15765	322	6	,	,	PUNCT
fcis-15765	322	7	fergus	fergus	PROPN
fcis-15765	322	8	r.	r.	PROPN
fcis-15765	322	9	predicting	predict	VERB
fcis-15765	322	10	depth	depth	NOUN
fcis-15765	322	11	,	,	PUNCT
fcis-15765	322	12	surface	surface	NOUN
fcis-15765	322	13	normals	normal	NOUN
fcis-15765	322	14	and	and	CCONJ
fcis-15765	322	15	semantic	semantic	ADJ
fcis-15765	322	16	labels	label	NOUN
fcis-15765	322	17	with	with	ADP
fcis-15765	322	18	a	a	DET
fcis-15765	322	19	common	common	ADJ
fcis-15765	322	20	multi	multi	ADJ
fcis-15765	322	21	-	-	ADJ
fcis-15765	322	22	scale	scale	ADJ
fcis-15765	322	23	convolutional	convolutional	ADJ
fcis-15765	322	24	architecture[c]//proceedings	architecture[c]//proceeding	NOUN
fcis-15765	322	25	of	of	ADP
fcis-15765	322	26	the	the	DET
fcis-15765	322	27	ieee	ieee	NOUN
fcis-15765	322	28	international	international	PROPN
fcis-15765	322	29	conference	conference	NOUN
fcis-15765	322	30	on	on	ADP
fcis-15765	322	31	computer	computer	NOUN
fcis-15765	322	32	vision	vision	NOUN
fcis-15765	322	33	.	.	PUNCT
fcis-15765	323	1	2015	2015	NUM
fcis-15765	323	2	:	:	PUNCT
fcis-15765	323	3	2650	2650	NUM
fcis-15765	323	4	-	-	SYM
fcis-15765	323	5	2658	2658	NUM
fcis-15765	323	6	.	.	PUNCT
fcis-15765	324	1	[	[	X
fcis-15765	324	2	39	39	NUM
fcis-15765	324	3	]	]	PUNCT
fcis-15765	324	4	ladicky	ladicky	PROPN
fcis-15765	324	5	l	l	PROPN
fcis-15765	324	6	,	,	PUNCT
fcis-15765	324	7	shi	shi	PROPN
fcis-15765	324	8	j	j	PROPN
fcis-15765	324	9	,	,	PUNCT
fcis-15765	324	10	pollefeys	pollefeys	PROPN
fcis-15765	324	11	m.	m.	NOUN
fcis-15765	324	12	pulling	pull	VERB
fcis-15765	324	13	things	thing	NOUN
fcis-15765	324	14	out	out	ADP
fcis-15765	324	15	of	of	ADP
fcis-15765	324	16	perspective[c]//proceedings	perspective[c]//proceeding	NOUN
fcis-15765	324	17	of	of	ADP
fcis-15765	324	18	the	the	DET
fcis-15765	324	19	ieee	ieee	NOUN
fcis-15765	324	20	conference	conference	NOUN
fcis-15765	324	21	on	on	ADP
fcis-15765	324	22	computer	computer	NOUN
fcis-15765	324	23	vision	vision	NOUN
fcis-15765	324	24	and	and	CCONJ
fcis-15765	324	25	pattern	pattern	NOUN
fcis-15765	324	26	recognition	recognition	NOUN
fcis-15765	324	27	.	.	PUNCT
fcis-15765	325	1	2014	2014	NUM
fcis-15765	325	2	:	:	PUNCT
fcis-15765	325	3	89	89	NUM
fcis-15765	325	4	-	-	SYM
fcis-15765	325	5	96	96	NUM
fcis-15765	325	6	.	.	PUNCT
fcis-15765	326	1	[	[	X
fcis-15765	326	2	40	40	NUM
fcis-15765	326	3	]	]	X
fcis-15765	326	4	liu	liu	PROPN
fcis-15765	326	5	m	m	PROPN
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fcis-15765	326	11	x.	x.	NOUN
fcis-15765	326	12	discrete	discrete	ADJ
fcis-15765	326	13	-	-	PUNCT
fcis-15765	326	14	continuous	continuous	ADJ
fcis-15765	326	15	depth	depth	NOUN
fcis-15765	326	16	estimation	estimation	NOUN
fcis-15765	326	17	from	from	ADP
fcis-15765	326	18	a	a	DET
fcis-15765	326	19	single	single	ADJ
fcis-15765	326	20	image[c]//proceedings	image[c]//proceeding	NOUN
fcis-15765	326	21	of	of	ADP
fcis-15765	326	22	the	the	DET
fcis-15765	326	23	ieee	ieee	NOUN
fcis-15765	326	24	conference	conference	NOUN
fcis-15765	326	25	on	on	ADP
fcis-15765	326	26	computer	computer	NOUN
fcis-15765	326	27	vision	vision	NOUN
fcis-15765	326	28	and	and	CCONJ
fcis-15765	326	29	pattern	pattern	NOUN
fcis-15765	326	30	recognition	recognition	NOUN
fcis-15765	326	31	.	.	PUNCT
fcis-15765	327	1	2014	2014	NUM
fcis-15765	327	2	:	:	PUNCT
fcis-15765	328	1	716	716	NUM
fcis-15765	328	2	-	-	SYM
fcis-15765	328	3	723	723	NUM
fcis-15765	328	4	.	.	PUNCT
fcis-15765	329	1	[	[	X
fcis-15765	329	2	41	41	NUM
fcis-15765	329	3	]	]	X
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fcis-15765	329	5	a	a	PRON
fcis-15765	329	6	,	,	PUNCT
fcis-15765	329	7	shazeer	shazeer	NOUN
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fcis-15765	329	9	,	,	PUNCT
fcis-15765	329	10	parmar	parmar	PROPN
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fcis-15765	329	12	,	,	PUNCT
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fcis-15765	329	14	al	al	PROPN
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fcis-15765	329	17	is	be	AUX
fcis-15765	329	18	all	all	PRON
fcis-15765	329	19	you	you	PRON
fcis-15765	329	20	need[j	need[j	VERB
fcis-15765	329	21	]	]	PUNCT
fcis-15765	329	22	.	.	PUNCT
fcis-15765	330	1	advances	advance	NOUN
fcis-15765	330	2	in	in	ADP
fcis-15765	330	3	neural	neural	ADJ
fcis-15765	330	4	information	information	NOUN
fcis-15765	330	5	processing	processing	NOUN
fcis-15765	330	6	systems	system	NOUN
fcis-15765	330	7	,	,	PUNCT
fcis-15765	330	8	2017	2017	NUM
fcis-15765	330	9	,	,	PUNCT
fcis-15765	330	10	30	30	NUM
fcis-15765	330	11	.	.	PUNCT
fcis-15765	331	1	[	[	X
fcis-15765	331	2	42	42	NUM
fcis-15765	331	3	]	]	X
fcis-15765	331	4	ranftl	ranftl	NOUN
fcis-15765	331	5	r	r	PROPN
fcis-15765	331	6	,	,	PUNCT
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fcis-15765	331	9	,	,	PUNCT
fcis-15765	331	10	koltun	koltun	PROPN
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fcis-15765	332	4	for	for	ADP
fcis-15765	332	5	dense	dense	ADJ
fcis-15765	332	6	prediction	prediction	NOUN
fcis-15765	333	1	[	[	X
fcis-15765	333	2	c]//	c]//	ADJ
fcis-15765	333	3	proceedings	proceeding	NOUN
fcis-15765	333	4	of	of	ADP
fcis-15765	333	5	the	the	DET
fcis-15765	333	6	ieee	ieee	NOUN
fcis-15765	333	7	/	/	SYM
fcis-15765	333	8	cvf	cvf	NOUN
fcis-15765	333	9	international	international	ADJ
fcis-15765	333	10	conference	conference	NOUN
fcis-15765	333	11	on	on	ADP
fcis-15765	333	12	computer	computer	NOUN
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fcis-15765	333	14	.	.	PUNCT
fcis-15765	334	1	2021	2021	NUM
fcis-15765	334	2	:	:	PUNCT
fcis-15765	334	3	1217912188	1217912188	NUM
fcis-15765	334	4	.	.	PUNCT
fcis-15765	335	1	[	[	X
fcis-15765	335	2	43	43	NUM
fcis-15765	335	3	]	]	X
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fcis-15765	335	7	,	,	PUNCT
fcis-15765	335	8	alhashim	alhashim	PROPN
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fcis-15765	335	10	,	,	PUNCT
fcis-15765	335	11	wonka	wonka	PROPN
fcis-15765	335	12	p.	p.	PROPN
fcis-15765	335	13	adabins	adabin	VERB
fcis-15765	335	14	:	:	PUNCT
fcis-15765	336	1	depth	depth	NOUN
fcis-15765	336	2	estimation	estimation	NOUN
fcis-15765	336	3	using	use	VERB
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fcis-15765	336	6	of	of	ADP
fcis-15765	336	7	the	the	DET
fcis-15765	336	8	ieee	ieee	NOUN
fcis-15765	336	9	/	/	SYM
fcis-15765	336	10	cvf	cvf	NOUN
fcis-15765	336	11	conference	conference	NOUN
fcis-15765	336	12	on	on	ADP
fcis-15765	336	13	computer	computer	NOUN
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fcis-15765	336	16	pattern	pattern	NOUN
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fcis-15765	336	18	.	.	PUNCT
fcis-15765	337	1	2021	2021	NUM
fcis-15765	337	2	:	:	PUNCT
fcis-15765	337	3	4009	4009	NUM
fcis-15765	337	4	-	-	SYM
fcis-15765	337	5	4018	4018	NUM
fcis-15765	337	6	.	.	PUNCT
fcis-15765	338	1	[	[	X
fcis-15765	338	2	44	44	NUM
fcis-15765	338	3	]	]	PUNCT
fcis-15765	338	4	xie	xie	PROPN
fcis-15765	338	5	j	j	PROPN
fcis-15765	338	6	,	,	PUNCT
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fcis-15765	338	8	r	r	NOUN
fcis-15765	338	9	,	,	PUNCT
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fcis-15765	338	11	a.	a.	NOUN
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fcis-15765	338	13	:	:	PUNCT
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fcis-15765	338	17	-	-	PUNCT
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fcis-15765	338	23	convolutional	convolutional	ADJ
fcis-15765	338	24	neural	neural	ADJ
fcis-15765	338	25	networks	network	NOUN
fcis-15765	338	26	[	[	X
fcis-15765	338	27	c]//	c]//	PROPN
fcis-15765	338	28	computer	computer	NOUN
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fcis-15765	338	30	–	–	PUNCT
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fcis-15765	338	33	:	:	PUNCT
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fcis-15765	338	36	conference	conference	PROPN
fcis-15765	338	37	,	,	PUNCT
fcis-15765	338	38	amsterdam	amsterdam	PROPN
fcis-15765	338	39	,	,	PUNCT
fcis-15765	338	40	the	the	DET
fcis-15765	338	41	netherlands	netherlands	PROPN
fcis-15765	338	42	,	,	PUNCT
fcis-15765	338	43	october	october	PROPN
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fcis-15765	338	45	,	,	PUNCT
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fcis-15765	338	47	,	,	PUNCT
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fcis-15765	338	49	,	,	PUNCT
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fcis-15765	339	5	2016	2016	NUM
fcis-15765	339	6	:	:	PUNCT
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fcis-15765	339	8	-	-	SYM
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fcis-15765	340	1	[	[	X
fcis-15765	340	2	45	45	NUM
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fcis-15765	340	16	.	.	PUNCT
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fcis-15765	341	4	information	information	NOUN
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fcis-15765	341	7	,	,	PUNCT
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fcis-15765	343	22	.	.	PUNCT
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fcis-15765	344	2	:	:	PUNCT
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fcis-15765	344	6	.	.	PUNCT
fcis-15765	345	1	[	[	X
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fcis-15765	345	20	[	[	X
fcis-15765	345	21	c]//	c]//	ADJ
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fcis-15765	345	34	recognition	recognition	NOUN
fcis-15765	345	35	.	.	PUNCT
fcis-15765	346	1	2019	2019	NUM
fcis-15765	346	2	:	:	PUNCT
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fcis-15765	346	4	-	-	SYM
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fcis-15765	346	6	.	.	PUNCT
fcis-15765	347	1	[	[	X
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fcis-15765	348	2	:	:	PUNCT
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fcis-15765	348	4	.	.	PUNCT
fcis-15765	349	1	[	[	X
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fcis-15765	350	1	a	a	DET
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fcis-15765	350	28	.	.	PUNCT
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fcis-15765	351	2	:	:	PUNCT
fcis-15765	351	3	4040	4040	NUM
fcis-15765	351	4	-	-	SYM
fcis-15765	351	5	4048	4048	NUM
fcis-15765	351	6	.	.	PUNCT
fcis-15765	352	1	[	[	X
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fcis-15765	352	6	,	,	PUNCT
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fcis-15765	352	21	constraints	constraint	NOUN
fcis-15765	352	22	[	[	PUNCT
fcis-15765	352	23	c]//	c]//	PROPN
fcis-15765	352	24	2019	2019	NUM
fcis-15765	352	25	ieee	ieee	NOUN
fcis-15765	352	26	winter	winter	NOUN
fcis-15765	352	27	conference	conference	NOUN
fcis-15765	352	28	on	on	ADP
fcis-15765	352	29	applications	application	NOUN
fcis-15765	352	30	of	of	ADP
fcis-15765	352	31	computer	computer	NOUN
fcis-15765	352	32	vision	vision	NOUN
fcis-15765	352	33	(	(	PUNCT
fcis-15765	352	34	wacv	wacv	NOUN
fcis-15765	352	35	)	)	PUNCT
fcis-15765	352	36	.	.	PUNCT
fcis-15765	353	1	ieee	ieee	NOUN
fcis-15765	353	2	,	,	PUNCT
fcis-15765	353	3	2019	2019	NUM
fcis-15765	353	4	:	:	PUNCT
fcis-15765	353	5	2087	2087	NUM
fcis-15765	353	6	-	-	SYM
fcis-15765	353	7	2096	2096	NUM
fcis-15765	353	8	.	.	PUNCT
fcis-15765	354	1	[	[	X
fcis-15765	354	2	51	51	NUM
fcis-15765	354	3	]	]	X
fcis-15765	354	4	klodt	klodt	PROPN
fcis-15765	354	5	m	m	PROPN
fcis-15765	354	6	,	,	PUNCT
fcis-15765	354	7	vedaldi	vedaldi	PROPN
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fcis-15765	354	10	the	the	DET
fcis-15765	354	11	new	new	ADJ
fcis-15765	354	12	with	with	ADP
fcis-15765	354	13	the	the	DET
fcis-15765	354	14	old	old	ADJ
fcis-15765	354	15	:	:	PUNCT
fcis-15765	354	16	learning	learn	VERB
fcis-15765	354	17	sfm	sfm	PROPN
fcis-15765	354	18	from	from	ADP
fcis-15765	354	19	sfm[c]//proceedings	sfm[c]//proceeding	NOUN
fcis-15765	354	20	of	of	ADP
fcis-15765	354	21	the	the	DET
fcis-15765	354	22	european	european	ADJ
fcis-15765	354	23	conference	conference	PROPN
fcis-15765	354	24	on	on	ADP
fcis-15765	354	25	computer	computer	NOUN
fcis-15765	354	26	vision	vision	NOUN
fcis-15765	354	27	(	(	PUNCT
fcis-15765	354	28	eccv	eccv	ADV
fcis-15765	354	29	)	)	PUNCT
fcis-15765	354	30	.	.	PUNCT
fcis-15765	355	1	2018	2018	NUM
fcis-15765	355	2	:	:	PUNCT
fcis-15765	355	3	698	698	NUM
fcis-15765	355	4	-	-	SYM
fcis-15765	355	5	713	713	NUM
fcis-15765	355	6	.	.	PUNCT
fcis-15765	356	1	[	[	X
fcis-15765	356	2	52	52	NUM
fcis-15765	356	3	]	]	X
fcis-15765	356	4	mur	mur	PROPN
fcis-15765	356	5	-	-	PROPN
fcis-15765	356	6	artal	artal	ADJ
fcis-15765	356	7	r	r	PROPN
fcis-15765	356	8	,	,	PUNCT
fcis-15765	356	9	tardós	tardós	PROPN
fcis-15765	356	10	j	j	PROPN
fcis-15765	356	11	d.	d.	PROPN
fcis-15765	356	12	orb	orb	PROPN
fcis-15765	356	13	-	-	PUNCT
fcis-15765	356	14	slam2	slam2	NOUN
fcis-15765	356	15	:	:	PUNCT
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fcis-15765	356	17	open	open	ADJ
fcis-15765	356	18	-	-	PUNCT
fcis-15765	356	19	source	source	NOUN
fcis-15765	356	20	slam	slam	NOUN
fcis-15765	356	21	system	system	NOUN
fcis-15765	356	22	for	for	ADP
fcis-15765	356	23	monocular	monocular	ADJ
fcis-15765	356	24	,	,	PUNCT
fcis-15765	356	25	stereo	stereo	ADJ
fcis-15765	356	26	,	,	PUNCT
fcis-15765	356	27	and	and	CCONJ
fcis-15765	356	28	rgb	rgb	PROPN
fcis-15765	356	29	-	-	PROPN
fcis-15765	356	30	d	d	PROPN
fcis-15765	356	31	cameras[j	cameras[j	PROPN
fcis-15765	356	32	]	]	PUNCT
fcis-15765	356	33	.	.	PUNCT
fcis-15765	357	1	ieee	ieee	NOUN
fcis-15765	357	2	transactions	transaction	NOUN
fcis-15765	357	3	on	on	ADP
fcis-15765	357	4	robotics	robotic	NOUN
fcis-15765	357	5	,	,	PUNCT
fcis-15765	357	6	2017	2017	NUM
fcis-15765	357	7	,	,	PUNCT
fcis-15765	357	8	33(5	33(5	NUM
fcis-15765	357	9	):	):	PUNCT
fcis-15765	357	10	1255	1255	NUM
fcis-15765	357	11	-	-	SYM
fcis-15765	357	12	1262	1262	NUM
fcis-15765	357	13	.	.	PUNCT
fcis-15765	358	1	[	[	X
fcis-15765	358	2	53	53	NUM
fcis-15765	358	3	]	]	PUNCT
fcis-15765	358	4	aleotti	aleotti	X
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fcis-15765	358	6	,	,	PUNCT
fcis-15765	358	7	tosi	tosi	PROPN
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fcis-15765	358	9	,	,	PUNCT
fcis-15765	358	10	poggi	poggi	VERB
fcis-15765	358	11	m	m	PROPN
fcis-15765	358	12	,	,	PUNCT
fcis-15765	358	13	et	et	PROPN
fcis-15765	358	14	al	al	PROPN
fcis-15765	358	15	.	.	PROPN
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fcis-15765	358	17	adversarial	adversarial	ADJ
fcis-15765	358	18	networks	network	NOUN
fcis-15765	358	19	for	for	ADP
fcis-15765	358	20	unsupervised	unsupervised	ADJ
fcis-15765	358	21	monocular	monocular	ADJ
fcis-15765	358	22	depth	depth	NOUN
fcis-15765	358	23	prediction[c]//	prediction[c]//	PROPN
fcis-15765	358	24	proceedings	proceeding	NOUN
fcis-15765	358	25	of	of	ADP
fcis-15765	358	26	the	the	DET
fcis-15765	358	27	european	european	PROPN
fcis-15765	358	28	conference	conference	PROPN
fcis-15765	358	29	on	on	ADP
fcis-15765	358	30	computer	computer	NOUN
fcis-15765	358	31	vision	vision	NOUN
fcis-15765	358	32	(	(	PUNCT
fcis-15765	358	33	eccv	eccv	ADJ
fcis-15765	358	34	)	)	PUNCT
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fcis-15765	358	36	.	.	PUNCT
fcis-15765	359	1	2018	2018	NUM
fcis-15765	359	2	:	:	PUNCT
fcis-15765	359	3	0	0	NUM
fcis-15765	359	4	-	-	SYM
fcis-15765	359	5	0	0	NUM
fcis-15765	359	6	.	.	PUNCT
fcis-15765	360	1	[	[	X
fcis-15765	360	2	54	54	NUM
fcis-15765	360	3	]	]	PUNCT
fcis-15765	360	4	mehta	mehta	PROPN
fcis-15765	360	5	i	i	PROPN
fcis-15765	360	6	,	,	PUNCT
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fcis-15765	360	8	p	p	X
fcis-15765	360	9	,	,	PUNCT
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fcis-15765	360	11	p	p	PROPN
fcis-15765	360	12	j.	j.	PROPN
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fcis-15765	360	14	adversarial	adversarial	ADJ
fcis-15765	360	15	training	training	NOUN
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fcis-15765	360	18	monocular	monocular	ADJ
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fcis-15765	360	21	[	[	X
fcis-15765	360	22	c]//	c]//	PROPN
fcis-15765	360	23	2018	2018	NUM
fcis-15765	360	24	international	international	ADJ
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fcis-15765	360	27	3d	3d	PROPN
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fcis-15765	360	29	(	(	PUNCT
fcis-15765	360	30	3dv	3dv	NOUN
fcis-15765	360	31	)	)	PUNCT
fcis-15765	360	32	.	.	PUNCT
fcis-15765	361	1	ieee	ieee	NOUN
fcis-15765	361	2	,	,	PUNCT
fcis-15765	361	3	2018	2018	NUM
fcis-15765	361	4	:	:	PUNCT
fcis-15765	361	5	314	314	NUM
fcis-15765	361	6	-	-	SYM
fcis-15765	361	7	323	323	NUM
fcis-15765	361	8	.	.	PUNCT
fcis-15765	362	1	[	[	X
fcis-15765	362	2	55	55	NUM
fcis-15765	362	3	]	]	X
fcis-15765	362	4	zhao	zhao	PROPN
fcis-15765	362	5	c	c	PROPN
fcis-15765	362	6	,	,	PUNCT
fcis-15765	362	7	sun	sun	PROPN
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fcis-15765	362	9	,	,	PUNCT
fcis-15765	362	10	zhang	zhang	PROPN
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fcis-15765	362	12	,	,	PUNCT
fcis-15765	362	13	et	et	PROPN
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fcis-15765	362	15	.	.	PROPN
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fcis-15765	362	20	on	on	ADP
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fcis-15765	362	23	:	:	PUNCT
fcis-15765	362	24	an	an	DET
fcis-15765	362	25	overview[j	overview[j	PROPN
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fcis-15765	363	4	sciences	science	NOUN
fcis-15765	363	5	,	,	PUNCT
fcis-15765	363	6	2020	2020	NUM
fcis-15765	363	7	,	,	PUNCT
fcis-15765	363	8	63(9	63(9	NUM
fcis-15765	363	9	):	):	PUNCT
fcis-15765	363	10	1612	1612	NUM
fcis-15765	363	11	-	-	SYM
fcis-15765	363	12	1627	1627	NUM
fcis-15765	363	13	.	.	PUNCT
fcis-15765	364	1	[	[	X
fcis-15765	364	2	56	56	NUM
fcis-15765	364	3	]	]	X
fcis-15765	364	4	masoumian	masoumian	PROPN
fcis-15765	364	5	a	a	PROPN
fcis-15765	364	6	,	,	PUNCT
fcis-15765	364	7	rashwan	rashwan	PROPN
fcis-15765	364	8	h	h	PROPN
fcis-15765	364	9	a	a	PROPN
fcis-15765	364	10	,	,	PUNCT
fcis-15765	364	11	cristiano	cristiano	PROPN
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fcis-15765	364	13	,	,	PUNCT
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fcis-15765	364	16	.	.	PROPN
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fcis-15765	364	18	depth	depth	NOUN
fcis-15765	364	19	estimation	estimation	NOUN
fcis-15765	364	20	using	use	VERB
fcis-15765	364	21	deep	deep	ADJ
fcis-15765	364	22	learning	learning	NOUN
fcis-15765	364	23	:	:	PUNCT
fcis-15765	364	24	a	a	DET
fcis-15765	364	25	review[j	review[j	PROPN
fcis-15765	364	26	]	]	PUNCT
fcis-15765	364	27	.	.	PUNCT
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fcis-15765	365	2	,	,	PUNCT
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fcis-15765	365	4	,	,	PUNCT
fcis-15765	365	5	22(14	22(14	NUM
fcis-15765	365	6	):	):	PUNCT
fcis-15765	365	7	5353	5353	NUM
fcis-15765	365	8	.	.	PUNCT