MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
CVPR 2024(2024)
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.
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