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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2407.07789 |
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| _version_ | 1866910526303371264 |
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| author | Lu, Xiaoyong Du, Songlin |
| author_facet | Lu, Xiaoyong Du, Songlin |
| contents | Current feature matching methods prioritize improving modeling capabilities to better align outputs with ground-truth matches, which are the theoretical upper bound on matching results, metaphorically depicted as the "ceiling". However, these enhancements fail to address the underlying issues that directly hinder ground-truth matches, including the scarcity of matchable points in small scale images, matching conflicts in dense methods, and the keypoint-repeatability reliance in sparse methods. We propose a novel feature matching method named RCM, which Raises the Ceiling of Matching from three aspects. 1) RCM introduces a dynamic view switching mechanism to address the scarcity of matchable points in source images by strategically switching image pairs. 2) RCM proposes a conflict-free coarse matching module, addressing matching conflicts in the target image through a many-to-one matching strategy. 3) By integrating the semi-sparse paradigm and the coarse-to-fine architecture, RCM preserves the benefits of both high efficiency and global search, mitigating the reliance on keypoint repeatability. As a result, RCM enables more matchable points in the source image to be matched in an exhaustive and conflict-free manner in the target image, leading to a substantial 260% increase in ground-truth matches. Comprehensive experiments show that RCM exhibits remarkable performance and efficiency in comparison to state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Raising the Ceiling: Conflict-Free Local Feature Matching with Dynamic View Switching Lu, Xiaoyong Du, Songlin Computer Vision and Pattern Recognition Current feature matching methods prioritize improving modeling capabilities to better align outputs with ground-truth matches, which are the theoretical upper bound on matching results, metaphorically depicted as the "ceiling". However, these enhancements fail to address the underlying issues that directly hinder ground-truth matches, including the scarcity of matchable points in small scale images, matching conflicts in dense methods, and the keypoint-repeatability reliance in sparse methods. We propose a novel feature matching method named RCM, which Raises the Ceiling of Matching from three aspects. 1) RCM introduces a dynamic view switching mechanism to address the scarcity of matchable points in source images by strategically switching image pairs. 2) RCM proposes a conflict-free coarse matching module, addressing matching conflicts in the target image through a many-to-one matching strategy. 3) By integrating the semi-sparse paradigm and the coarse-to-fine architecture, RCM preserves the benefits of both high efficiency and global search, mitigating the reliance on keypoint repeatability. As a result, RCM enables more matchable points in the source image to be matched in an exhaustive and conflict-free manner in the target image, leading to a substantial 260% increase in ground-truth matches. Comprehensive experiments show that RCM exhibits remarkable performance and efficiency in comparison to state-of-the-art methods. |
| title | Raising the Ceiling: Conflict-Free Local Feature Matching with Dynamic View Switching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.07789 |