id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ispiv-120	Lagemann, Christian; Klaas, Michael; Schröder, Wolfgang	Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry	2021	10	.pdf	application/pdf	5158	216	43	In this light, PIV images pose an even greater chal- lenge on unsuspervised optical flow networks since they contain many, but tiny and almost identical image features - the particles - and hence, provide similar image patterns within the local neighborhood impeding the prediction of the physical correct displacement. Therefore, we propose URAFT-PIV, an unsupervised deep neural network architecture for optical flow estimation in PIV applications and show that our combination of state-of-the-art deep learning pipelines and unsupervised learning achieves a new state-of-the-art accuracy for unsupervised PIV networks while performing similar to supervisedly trained LiteFlowNet based competitors.	cache/ispiv-120.pdf	txt/ispiv-120.txt