Feature matching is a crucial technique in computer vision. Essentially, it
can be considered as a searching problem to establish correspondences between
images. The key challenge in this task lies in the lack of a well-defined
search space, leading to inaccurate point matching of current methods. In
pursuit of a reasonable matching search space, this paper introduces a
hierarchical feature matching framework: Area to Point Matching (A2PM), to
first find semantic area matches between images, and then perform point
matching on area matches, thus setting the search space as the area matches
with salient features to achieve high matching precision. This proper search
space of A2PM framework also alleviates the accuracy limitation in
state-of-the-art Transformer-based matching methods. To realize this framework,
we further propose Semantic and Geometry Area Matching (SGAM) method, which
utilizes semantic prior and geometry consistency to establish accurate area
matches between images. By integrating SGAM with off-the-shelf
Transformer-based matchers, our feature matching methods, adopting the A2PM
framework, achieve encouraging precision improvements in massive point matching
and pose estimation experiments for present arts.

本文介绍了一种基于面积匹配的层次型特征匹配框架：A2PM，通过先找到图像间语义区域匹配，再在区域匹配上执行点匹配，将搜索空间设置为具有显著特征的区域匹配，从而实现高匹配精度，并使用语义和几何区域匹配方法（SGAM）来建立精确无误的区域匹配。通过将 SGAM 与现有的 Transformer-based 匹配器结合使用，我们的特征匹配方法在大规模点匹配和姿态估计实验中取得了令人鼓舞的精确度改进。