SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation
Recent self-supervised stereo matching methods have made significant progress. They typically rely on the photometric consistency assumption, which presumes corresponding points across views share the same appearance. However, this assumption could be compromised by real-world disturbances, resulting in invalid supervisory signals and a significant accuracy gap compared to supervised methods. To address this issue, we propose SMFormer, a framework integrating more reliable self-supervision guided by the Vision Foundation Model (VFM) and data augmentation. We first incorporate the VFM with the Feature Pyramid Network (FPN), providing a discriminative and robust feature representation again...
| Authors | Yun Wang, Zhengjie Yang, Jiahao Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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