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.

翻译：基于学习的立体匹配技术已取得显著进展。然而，现有方法在特征通道生成过程中不可避免地丢失几何结构信息，导致边缘细节匹配误差。针对该问题，本文设计了母题通道注意力立体匹配网络（MoCha-Stereo）。我们提出母题通道相关体（MCCV），通过将捕捉特征通道中共同几何结构的母题通道投影至特征图和代价体上，实现更精确的边缘匹配代价计算。此外，重建误差图中潜在特征通道的边缘变化同样影响细节匹配，为此我们提出重建误差母题惩罚模块（REMP），通过整合重建误差中典型通道特征的频率信息，进一步优化全分辨率视差估计。MoCha-Stereo在KITTI-2015和KITTI-2012反射排行榜上位列第一，并在多视图立体匹配中展现出卓越性能。代码开源地址：https://github.com/ZYangChen/MoCha-Stereo。