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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.21364 |
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| _version_ | 1866911024584589312 |
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| author | Cheng, Zhixin Deng, Jiacheng Li, Xinjun Yin, Xiaotian Liao, Bohao Yin, Baoqun Yang, Wenfei Zhang, Tianzhu |
| author_facet | Cheng, Zhixin Deng, Jiacheng Li, Xinjun Yin, Xiaotian Liao, Bohao Yin, Baoqun Yang, Wenfei Zhang, Tianzhu |
| contents | Detection-free methods typically follow a coarse-to-fine pipeline, extracting image and point cloud features for patch-level matching and refining dense pixel-to-point correspondences. However, differences in feature channel attention between images and point clouds may lead to degraded matching results, ultimately impairing registration accuracy. Furthermore, similar structures in the scene could lead to redundant correspondences in cross-modal matching. To address these issues, we propose Channel Adaptive Adjustment Module (CAA) and Global Optimal Selection Module (GOS). CAA enhances intra-modal features and suppresses cross-modal sensitivity, while GOS replaces local selection with global optimization. Experiments on RGB-D Scenes V2 and 7-Scenes demonstrate the superiority of our method, achieving state-of-the-art performance in image-to-point cloud registration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21364 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection Cheng, Zhixin Deng, Jiacheng Li, Xinjun Yin, Xiaotian Liao, Bohao Yin, Baoqun Yang, Wenfei Zhang, Tianzhu Computer Vision and Pattern Recognition Artificial Intelligence Detection-free methods typically follow a coarse-to-fine pipeline, extracting image and point cloud features for patch-level matching and refining dense pixel-to-point correspondences. However, differences in feature channel attention between images and point clouds may lead to degraded matching results, ultimately impairing registration accuracy. Furthermore, similar structures in the scene could lead to redundant correspondences in cross-modal matching. To address these issues, we propose Channel Adaptive Adjustment Module (CAA) and Global Optimal Selection Module (GOS). CAA enhances intra-modal features and suppresses cross-modal sensitivity, while GOS replaces local selection with global optimization. Experiments on RGB-D Scenes V2 and 7-Scenes demonstrate the superiority of our method, achieving state-of-the-art performance in image-to-point cloud registration. |
| title | CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.21364 |