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 by projecting motif channels, which capture common geometric structures in feature channels, onto feature maps and cost volumes. In addition, edge variations in the potential feature channels of the reconstruction error map also affect edge texture matching. To further refine the full-resolution disparity details, we propose the Reconstruction Error Motif Penalty (REMP) module, which 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. The structure of MoCha-Stereo also shows excellent performance in Multi-View Stereo.