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 framework for scale-imbalanced image matching. It uses symmetric cross-view interaction to fuse the two feature representations and identify their shared spatial support. From the fused features, SAMatcher predicts dense co-visible masks and bounding boxes. These predictions define view-specific cropping windows for downstream point-level matching. Built upon the Segment Anything Model (SAM), SAMatcher extends monocular region modeling to cross-view co-visibility reasoning. Point-sampled mask learning, box regression, and mask--box consistency jointly supervise its multi-granularity predictions. Extensive experiments follow an evaluation protocol centered on cross-view scale imbalance. The results show that SAMatcher consistently improves matching robustness across diverse matching pipelines. When integrated with RoMa, SAMatcher improves AUC@5 and mAA@5 by 5.58 and 4.97 points, respectively. The results demonstrate that cross-view fusion and dense co-visibility modeling provide reliable priors for matching under scale-induced quality imbalance. Code and project page are available at https://xupan.top/projects/samatcher