OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching

• URL: http://arxiv.org/abs/2302.05846v1
• Date: Sun, 12 Feb 2023 03:32:45 GMT
• Title: OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
• Authors: Kun Dai, Tao Xie, Ke Wang, Zhiqiang Jiang, Ruifeng Li, Lijun Zhao
• Abstract summary: We propose OAMatcher, a detector-free method that imitates humans behavior to generate dense and accurate matches. OAMatcher predicts overlapping areas to promote effective and clean global context aggregation. Comprehensive experiments demonstrate that OAMatcher outperforms the state-of-the-art methods on several benchmarks.
• Score: 9.006654114778073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behavior to generate dense and accurate matches. Firstly, OAMatcher predicts overlapping areas to promote effective and clean global context aggregation, with the key insight that humans focus on the overlapping areas instead of the entire images after multiple observations when matching keypoints in image pairs. Technically, we first perform global information integration across all keypoints to imitate the humans behavior of observing the entire images at the beginning of feature matching. Then, we propose Overlapping Areas Prediction Module (OAPM) to capture the keypoints in co-visible regions and conduct feature enhancement among them to simulate that humans transit the focus regions from the entire images to overlapping regions, hence realizeing effective information exchange without the interference coming from the keypoints in non overlapping areas. Besides, since humans tend to leverage probability to determine whether the match labels are correct or not, we propose a Match Labels Weight Strategy (MLWS) to generate the coefficients used to appraise the reliability of the ground-truth match labels, while alleviating the influence of measurement noise coming from the data. Moreover, we integrate depth-wise convolution into Tranformer encoder layers to ensure OAMatcher extracts local and global feature representation concurrently. Comprehensive experiments demonstrate that OAMatcher outperforms the state-of-the-art methods on several benchmarks, while exhibiting excellent robustness to extreme appearance variants. The source code is available at https://github.com/DK-HU/OAMatcher.

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