• Corpus ID: 220381395

# Wasserstein Distances for Stereo Disparity Estimation

@article{Garg2020WassersteinDF,
title={Wasserstein Distances for Stereo Disparity Estimation},
author={Divyansh Garg and Yan Wang and Bharath Hariharan and Mark E. Campbell and Kilian Q. Weinberger and Wei-Lun Chao},
journal={ArXiv},
year={2020},
volume={abs/2007.03085}
}
• Published 6 July 2020
• Computer Science
• ArXiv
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or disparity does not match any of these values. The fact that this distribution is usually learned indirectly through a regression loss causes further problems in ambiguous regions around object boundaries. We address these issues using a new neural network architecture that is capable of outputting arbitrary depth values…

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