Practical Deep Stereo (PDS): Toward Applications-friendly Deep Stereo Matching

Authors:
Stepan Tulyakov École polytechnique fédérale de Lausanne (EPFL)
Anton Ivanov EPFL
François Fleuret Idiap Research Institute

Introduction:

End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching.

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.

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