Papers › Wasserstein Distances for Stereo Disparity Estimation
Wasserstein Distances for Stereo Disparity Estimation
Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
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, and a new loss function that is derived from the Wasserstein distance between the true and the predicted distributions. We validate our approach on a variety of tasks, including stereo disparity and depth estimation, and the downstream 3D object detection. Our approach drastically reduces the error in ambiguous regions, especially around object boundaries that greatly affect the localization of objects in 3D, achieving the state-of-the-art in 3D object detection for autonomous driving. Our code will be available at https://github.com/Div99/W-Stereo-Disp.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection From Stereo Images | KITTI Cars Moderate | CDN-DSGN | AP75 | 54.2 | #3 of 12 | Archive leaderboard | report |
| Stereo Depth Estimation | KITTI2015 | CDN-GANet Deep | three pixel error | 1.92 | #3 of 7 | Archive leaderboard | report |
| Stereo Disparity Estimation | Scene Flow | CDN-GANet Deep | EPE | 0.7 | #4 of 7 | Archive leaderboard | report |
| Stereo Disparity Estimation | Scene Flow | CDN-GANet Deep | one pixel error | 7.7 | #4 of 7 | Archive leaderboard | report |
| Stereo Disparity Estimation | Scene Flow | CDN-GANet Deep | three pixel error | 2.98 | #4 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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