Publication

Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

François Fleuret, Stepan Tulyakov
2018
Conference Papers

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 trained for a given disparity range.

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Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

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