  Feature matching is a crucial technique in computer vision. A unified
perspective for this task is to treat it as a searching problem, aiming at an
efficient search strategy to narrow the search space to point matches between
images. One of the key aspects of search strategy is the search space, which in
current approaches is not carefully defined, resulting in limited matching
accuracy. This paper, thus, pays attention to the search space and proposes to
set the initial search space for point matching as the matched image areas
containing prominent semantic, named semantic area matches. This search space
favors point matching by salient features and alleviates the accuracy
limitation in recent Transformer-based matching methods. To achieve this search
space, we introduce a hierarchical feature matching framework: Area to Point
Matching (A2PM), to first find semantic area matches between images and later
perform point matching on area matches. We further propose Semantic and
Geometry Area Matching (SGAM) method to realize this framework, which utilizes
semantic prior and geometry consistency to establish accurate area matches
between images. By integrating SGAM with off-the-shelf state-of-the-art
matchers, our method, adopting the A2PM framework, achieves encouraging
precision improvements in massive point matching and pose estimation
experiments.
