The KITTI Vision Benchmark Suite

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 Channel 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 the reconstruction error map also
affect details matching, we propose the
Reconstruction Error Motif Penalty (REMP) module
to further refine the fullresolution disparity
estimation. REMP integrates the frequency
information of typical channel features from the
reconstruction error.

Iters=32

@inproceedings{mocha,
title={MoCha-Stereo: Motif Channel Attention
Network for Stereo Matching},
author={Chen, Ziyang and Long, Wei and
Yao, He and Zhang, Yongjun and Wang, Bingshu and
Qin, Yongbin and Wu, Jia},
booktitle={Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR)},
year={2024}
}