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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.22306 |
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| _version_ | 1866912162467807232 |
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| author | Zhang, Haomeng Yang, Chiao-An Yeh, Raymond A. |
| author_facet | Zhang, Haomeng Yang, Chiao-An Yeh, Raymond A. |
| contents | Multi-object 3D Grounding involves locating 3D boxes based on a given query phrase from a point cloud. It is a challenging and significant task with numerous applications in visual understanding, human-computer interaction, and robotics. To tackle this challenge, we introduce D-LISA, a two-stage approach incorporating three innovations. First, a dynamic vision module that enables a variable and learnable number of box proposals. Second, a dynamic camera positioning that extracts features for each proposal. Third, a language-informed spatial attention module that better reasons over the proposals to output the final prediction. Empirically, experiments show that our method outperforms the state-of-the-art methods on multi-object 3D grounding by 12.8% (absolute) and is competitive in single-object 3D grounding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22306 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention Zhang, Haomeng Yang, Chiao-An Yeh, Raymond A. Computer Vision and Pattern Recognition Multi-object 3D Grounding involves locating 3D boxes based on a given query phrase from a point cloud. It is a challenging and significant task with numerous applications in visual understanding, human-computer interaction, and robotics. To tackle this challenge, we introduce D-LISA, a two-stage approach incorporating three innovations. First, a dynamic vision module that enables a variable and learnable number of box proposals. Second, a dynamic camera positioning that extracts features for each proposal. Third, a language-informed spatial attention module that better reasons over the proposals to output the final prediction. Empirically, experiments show that our method outperforms the state-of-the-art methods on multi-object 3D grounding by 12.8% (absolute) and is competitive in single-object 3D grounding. |
| title | Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.22306 |