NeurIPS 2018poster149 citations
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
Stepan Tulyakov, Anton Ivanov, François Fleuret
Abstract
End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be fully re-trained to handle a different disparity range.
BibTeX
@inproceedings{NEURIPS2018_ade55409,
author = {Tulyakov, Stepan and Ivanov, Anton and Fleuret, Fran\c{c}ois},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/ade55409d1224074754035a5a937d2e0-Paper.pdf},
volume = {31},
year = {2018}
}