  Local feature matching is challenging due to textureless and repetitive
patterns. Existing methods focus on using appearance features and global
interaction and matching, while the importance of geometry priors in local
feature matching has not been fully exploited. Different from these methods, in
this paper, we delve into the importance of geometry prior and propose
Structured Epipolar Matcher (SEM) for local feature matching, which can
leverage the geometric information in an iterative matching way. The proposed
model enjoys several merits. First, our proposed Structured Feature Extractor
can model the relative positional relationship between pixels and
high-confidence anchor points. Second, our proposed Epipolar Attention and
Matching can filter out irrelevant areas by utilizing the epipolar constraint.
Extensive experimental results on five standard benchmarks demonstrate the
superior performance of our SEM compared to state-of-the-art methods. Project
page: https://sem2023.github.io.
