• 通过CNN多尺度特征提取与注意力机制检测影像对重叠区域，获取尺度先验指导下游宽基线影像匹配
• 创造性地将重叠估计问题转化为目标检测问题

(Submitted)
Establishing reliable point correspondences between images with significant scale differences is a persistent challenge in photogrammetry and remote sensing. The scale differences often cause semantic ambiguities and positional shifts in the estimated correspondences. Limited by the direct point correspondence framework, existing methods struggle to address the scale issue at the local feature level. To overcome this, we address the scale difference issue by detecting co-visible regions between image pairs and propose \textbf{SCoDe} (\textbf{S}cale-aware \textbf{Co}-visible region \textbf{De}tector) that identifies and aligns co-visible regions for highly robust hierarchical point correspondence matching. Specifically, SCoDe employs a novel Scale Head Attention mechanism to map and correlate features across multiple scale subspaces, and leverages a learnable query to gather information from extracted scale-aware features of both images for co-visible region detection. In this way, correspondences can be hierarchically determined from region-level to point-level with semantic and location uncertainty eliminated. Extensive experiments conducted on three challenging datasets demonstrate the clear superiority of SCoDe over state-of-the-art methods, improving point matching precision by 9.04\%. SCoDe also exhibits a notable advantage when dealing with images that have large scale differences.