STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matching
Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a deterministic regression problem, collapsing the multimodal distribution modeling into a single-point estimation. This formulation suffers from a regression-to-mean bias, frequently struggling with ambiguous regions. In contrast, we introduce a prior-guided generative framework that integrates deterministic matching regression and generative distribution modeling within a complementary formulation. Built upon this formulation, we introduce StereoFlow through three key components: (i) a two-stage progressive cascade matching network that progressi...
| Authors | Hao Wang, Haoran Geng, Xiaotong Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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