mlsanityData to 14 Oct 2026

Structured Epipolar Matcher for Local Feature Matching

arXiv 2303.16646 ↗First posted 29 Mar 2023cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 39 of its 53 references that are in the map. First posted on arXiv 29 Mar 2023.

Builds on

39 of 53 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 4.

8 papers.

  1. 2024Local Feature Matching Using Deep Learning: A Surveycited by 26
  2. 2024SRPose: Two-view Relative Pose Estimation with Sparse Keypointscited by 5
  3. 2023LGFCTR: Local and Global Feature Convolutional Transformer for Image Matchingcited by 1
  4. 2024Exploiting Motion Prior for Accurate Pose Estimation of Dashboard Camerascited by 1
  5. 2026Pixel-Accurate Epipolar Guided Matchingcited by 0
  6. 2026EpiDiffVO: Geometry-Aware Epipolar Diffusion for Robust Visual Odometrycited by 0
  7. 2026LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matchingcited by 0
  8. 2026Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectivescited by 0