SAMatcher: Dense Co-Visibility Modeling via Cross-View Fusion for Scale-Imbalance Image Matching
Reliable correspondence estimation supports image processing and 3D vision tasks, including Structure from Motion, visual localization, and image registration. Wide-baseline matching is difficult under cross-view scale imbalance. The same content may appear at different resolutions and spatial extents, creating unequal observation quality. Such pairs constitute low-quality multi-view data, even when each image is clear. Image information that is distinctive in one view may be weak or unavailable in the other. Existing methods rely on pixel- or patch-level appearance and do not explicitly identify where reliable evidence is shared across views. We propose SAMatcher, a modular co-visibility...
| Authors | Xu Pan, Zhen Pang, Qiyuan Ma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 June 2026 |
| Last updated at source | 22 September 2026 |
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