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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.26262 |
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| _version_ | 1866911548087205888 |
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| author | Cheng, Zhixin Deng, Jiacheng Li, Xinjun Liao, Bohao Liu, Li Yin, Xiaotian Yin, Baoqun Zhang, Tianzhu |
| author_facet | Cheng, Zhixin Deng, Jiacheng Li, Xinjun Liao, Bohao Liu, Li Yin, Xiaotian Yin, Baoqun Zhang, Tianzhu |
| contents | Image-to-point cloud registration methods typically follow a coarse-to-fine pipeline, extracting patch-level correspondences and refining them into dense pixel-to-point matches. However, in scenes with repetitive patterns, images often lack sufficient 3D structural cues and alignment with point clouds, leading to incorrect matches. Moreover, prior methods usually overlook structural consistency, limiting the full exploitation of correspondences. To address these issues, we propose two novel modules: the Local Geometry Enhancement (LGE) module and the Graph Distribution Consistency (GDC) module. LGE enhances both image and point cloud features with normal vectors, injecting geometric structure into image features to reduce mismatches. GDC constructs a graph from matched points to update features and explicitly constrain similarity distributions. Extensive experiments and ablations on two benchmarks, RGB-D Scenes v2 and 7-Scenes, demonstrate that our approach achieves state-of-the-art performance in image-to-point cloud registration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26262 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GLASS: Geometry-aware Local Alignment and Structure Synchronization Network for 2D-3D Registration Cheng, Zhixin Deng, Jiacheng Li, Xinjun Liao, Bohao Liu, Li Yin, Xiaotian Yin, Baoqun Zhang, Tianzhu Computer Vision and Pattern Recognition Machine Learning Image-to-point cloud registration methods typically follow a coarse-to-fine pipeline, extracting patch-level correspondences and refining them into dense pixel-to-point matches. However, in scenes with repetitive patterns, images often lack sufficient 3D structural cues and alignment with point clouds, leading to incorrect matches. Moreover, prior methods usually overlook structural consistency, limiting the full exploitation of correspondences. To address these issues, we propose two novel modules: the Local Geometry Enhancement (LGE) module and the Graph Distribution Consistency (GDC) module. LGE enhances both image and point cloud features with normal vectors, injecting geometric structure into image features to reduce mismatches. GDC constructs a graph from matched points to update features and explicitly constrain similarity distributions. Extensive experiments and ablations on two benchmarks, RGB-D Scenes v2 and 7-Scenes, demonstrate that our approach achieves state-of-the-art performance in image-to-point cloud registration. |
| title | GLASS: Geometry-aware Local Alignment and Structure Synchronization Network for 2D-3D Registration |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2603.26262 |