Papers › Pyramid Stereo Matching Network
Pyramid Stereo Matching Network
Jia-Ren Chang, Yong-Sheng Chen
Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in illposed regions. To tackle this problem, we propose PSMNet, a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume. The 3D CNN learns to regularize cost volume using stacked multiple hourglass networks in conjunction with intermediate supervision. The proposed approach was evaluated on several benchmark datasets. Our method ranked first in the KITTI 2012 and 2015 leaderboards before March 18, 2018. The codes of PSMNet are available at: https://github.com/JiaRenChang/PSMNet.
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Code
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Code Syntology ran Syntology
11 samples harvested; 3 ran; 0 honoured the contract we drafted; 8 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | Depth-LRCE | 1.809 | #5 of 5 | Archive leaderboard | report |
| Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | Depth-MAE | 2.509 | #5 of 5 | Archive leaderboard | report |
| Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | Depth-MARE | 0.176 | #5 of 5 | Archive leaderboard | report |
| Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | Depth-RMSE | 5.673 | #5 of 5 | Archive leaderboard | report |
| Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | Disp-MAE | 0.286 | #5 of 5 | Archive leaderboard | report |
| Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | Disp-MARE | 0.248 | #5 of 5 | Archive leaderboard | report |
| Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | Disp-RMSE | 0.496 | #5 of 5 | Archive leaderboard | report |
| Stereo-LiDAR Fusion | KITTI Depth Completion Validation | PSMNet | RMSE | 884 | #7 of 9 | 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.
Methods
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