HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911803396587520 |
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| author | Chen, Ying Liu, Yong Wu, Kai Nie, Qiang Xu, Shang Ma, Huifang Wang, Bing Wang, Chengjie |
| author_facet | Chen, Ying Liu, Yong Wu, Kai Nie, Qiang Xu, Shang Ma, Huifang Wang, Bing Wang, Chengjie |
| contents | Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands. To tackle this challenge, we present HCPM, an efficient and detector-free local feature-matching method that employs hierarchical pruning to optimize the matching pipeline. In contrast to recent detector-free methods that depend on an exhaustive set of coarse-level candidates for matching, HCPM selectively concentrates on a concise subset of informative candidates, resulting in fewer computational candidates and enhanced matching efficiency. The method comprises a self-pruning stage for selecting reliable candidates and an interactive-pruning stage that identifies correlated patches at the coarse level. Our results reveal that HCPM significantly surpasses existing methods in terms of speed while maintaining high accuracy. The source code will be made available upon publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12543 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching Chen, Ying Liu, Yong Wu, Kai Nie, Qiang Xu, Shang Ma, Huifang Wang, Bing Wang, Chengjie Computer Vision and Pattern Recognition Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands. To tackle this challenge, we present HCPM, an efficient and detector-free local feature-matching method that employs hierarchical pruning to optimize the matching pipeline. In contrast to recent detector-free methods that depend on an exhaustive set of coarse-level candidates for matching, HCPM selectively concentrates on a concise subset of informative candidates, resulting in fewer computational candidates and enhanced matching efficiency. The method comprises a self-pruning stage for selecting reliable candidates and an interactive-pruning stage that identifies correlated patches at the coarse level. Our results reveal that HCPM significantly surpasses existing methods in terms of speed while maintaining high accuracy. The source code will be made available upon publication. |
| title | HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.12543 |