  Feature matching is a crucial task in the field of computer vision, which
involves finding correspondences between images. Previous studies achieve
remarkable performance using learning-based feature comparison. However, the
pervasive presence of matching redundancy between images gives rise to
unnecessary and error-prone computations in these methods, imposing limitations
on their accuracy. To address this issue, we propose MESA, a novel approach to
establish precise area (or region) matches for efficient matching redundancy
reduction. MESA first leverages the advanced image understanding capability of
SAM, a state-of-the-art foundation model for image segmentation, to obtain
image areas with implicit semantic. Then, a multi-relational graph is proposed
to model the spatial structure of these areas and construct their scale
hierarchy. Based on graphical models derived from the graph, the area matching
is reformulated as an energy minimization task and effectively resolved.
Extensive experiments demonstrate that MESA yields substantial precision
improvement for multiple point matchers in indoor and outdoor downstream tasks,
e.g. +13.61% for DKM in indoor pose estimation.
