Papers › MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
Ziyang Chen, Wei Long, He Yao, Yongjun Zhang, Bingshu Wang, Yongbin Qin, Jia Wu
Learning-based stereo matching techniques have made significant progress. However, existing methods inevitably lose geometrical structure information during the feature channel generation process, resulting in edge detail mismatches. In this paper, the Motif Cha}nnel Attention Stereo Matching Network (MoCha-Stereo) is designed to address this problem. We provide the Motif Channel Correlation Volume (MCCV) to determine more accurate edge matching costs. MCCV is achieved by projecting motif channels, which capture common geometric structures in feature channels, onto feature maps and cost volumes. In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation. REMP integrates the frequency information of typical channel features from the reconstruction error. MoCha-Stereo ranks 1st on the KITTI-2015 and KITTI-2012 Reflective leaderboards. Our structure also shows excellent performance in Multi-View Stereo. Code is avaliable at https://github.com/ZYangChen/MoCha-Stereo.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Stereo Depth Estimation | KITTI 2015 | MoCha-Stereo | D1-all All | 1.53 | #1 of 2 | Archive leaderboard | report |
| Stereo Depth Estimation | KITTI 2015 | MoCha-Stereo | D1-all Noc | 1.44 | #1 of 2 | Archive leaderboard | report |
| Stereo Disparity Estimation | KITTI 2015 | MoCha-Stereo | D1-all | 1.53 | #1 of 2 | Archive leaderboard | report |
| Stereo Disparity Estimation | Middlebury 2014 | MoCha-V2 | D1 Error (2px) | 3.51 | #1 of 2 | 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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