MoCha-Stereo：面向立体匹配的基元通道注意力网络
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
AI总结：
针对现有立体匹配方法易丢失几何结构信息导致边缘细节失配的问题，提出MoCha-Stereo网络，通过MCCV和REMP模块优化视差估计，在KITTI相关基准上排名第一且在多视图立体匹配中表现优异。
摘要：
基于学习的立体匹配技术已取得显著进展，但现有方法在特征通道生成过程中不可避免会丢失几何结构信息，导致边缘细节失配。为解决该问题，本文设计了基元通道注意力立体匹配网络（Motif Channel Attention Stereo Matching Network, MoCha-Stereo）。我们提出基元通道相关体（Motif Channel Correlation Volume, MCCV）以获取更精准的边缘匹配置价，MCCV通过将捕捉特征通道中常见几何结构的基元通道投影到特征图和代价体上实现。此外，重建误差图的潜在特征通道中的边缘变化也会影响细节匹配，我们提出重建误差基元惩罚（Reconstruction Error Motif Penalty, REMP）模块以进一步优化全分辨率视差估计，REMP整合了重建误差中典型通道特征的频率信息。MoCha-Stereo在KITTI-2015和KITTI-2012反射基准排行榜上排名第一，该结构在多视图立体匹配中也表现出优异性能。代码可在https://github.com/ZYangChen/MoCha-Stereo获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
