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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.08966 |
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| _version_ | 1866910406859030528 |
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| author | Ma, Nan Wang, Mohan Han, Yiheng Liu, Yong-Jin |
| author_facet | Ma, Nan Wang, Mohan Han, Yiheng Liu, Yong-Jin |
| contents | Cross-modality point cloud registration is confronted with significant challenges due to inherent differences in modalities between different sensors. We propose a cross-modality point cloud registration framework FF-LOGO: a cross-modality point cloud registration method with feature filtering and local-global optimization. The cross-modality feature correlation filtering module extracts geometric transformation-invariant features from cross-modality point clouds and achieves point selection by feature matching. We also introduce a cross-modality optimization process, including a local adaptive key region aggregation module and a global modality consistency fusion optimization module. Experimental results demonstrate that our two-stage optimization significantly improves the registration accuracy of the feature association and selection module. Our method achieves a substantial increase in recall rate compared to the current state-of-the-art methods on the 3DCSR dataset, improving from 40.59% to 75.74%. Our code will be available at https://github.com/wangmohan17/FFLOGO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08966 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization Ma, Nan Wang, Mohan Han, Yiheng Liu, Yong-Jin Computer Vision and Pattern Recognition Cross-modality point cloud registration is confronted with significant challenges due to inherent differences in modalities between different sensors. We propose a cross-modality point cloud registration framework FF-LOGO: a cross-modality point cloud registration method with feature filtering and local-global optimization. The cross-modality feature correlation filtering module extracts geometric transformation-invariant features from cross-modality point clouds and achieves point selection by feature matching. We also introduce a cross-modality optimization process, including a local adaptive key region aggregation module and a global modality consistency fusion optimization module. Experimental results demonstrate that our two-stage optimization significantly improves the registration accuracy of the feature association and selection module. Our method achieves a substantial increase in recall rate compared to the current state-of-the-art methods on the 3DCSR dataset, improving from 40.59% to 75.74%. Our code will be available at https://github.com/wangmohan17/FFLOGO. |
| title | FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.08966 |