3 papers
cs.CV2026
SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation
Yun Wang, Zhengjie Yang, Jiahao Zheng +3
Recent self-supervised stereo matching methods have made significant progress. They typically rely on the photometric consistency assumption, which presumes corresponding points ac…
cs.CV2025
PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models
Yongjian Zhang, Longguang Wang, Kunhong Li +4
This work presents PanMatch, a versatile foundation model for robust correspondence matching. Unlike previous methods that rely on task-specific architectures and domain-specific f…
cs.CV2025
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
Yun Wang, Longguang Wang, Chenghao Zhang +6
Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due t…