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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.03077 |
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| _version_ | 1866915041321680896 |
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| author | Chang, Chun-Peng Wang, Shaoxiang Pagani, Alain Stricker, Didier |
| author_facet | Chang, Chun-Peng Wang, Shaoxiang Pagani, Alain Stricker, Didier |
| contents | 3D visual grounding involves matching natural language descriptions with their corresponding objects in 3D spaces. Existing methods often face challenges with accuracy in object recognition and struggle in interpreting complex linguistic queries, particularly with descriptions that involve multiple anchors or are view-dependent. In response, we present the MiKASA (Multi-Key-Anchor Scene-Aware) Transformer. Our novel end-to-end trained model integrates a self-attention-based scene-aware object encoder and an original multi-key-anchor technique, enhancing object recognition accuracy and the understanding of spatial relationships. Furthermore, MiKASA improves the explainability of decision-making, facilitating error diagnosis. Our model achieves the highest overall accuracy in the Referit3D challenge for both the Sr3D and Nr3D datasets, particularly excelling by a large margin in categories that require viewpoint-dependent descriptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding Chang, Chun-Peng Wang, Shaoxiang Pagani, Alain Stricker, Didier Computer Vision and Pattern Recognition 3D visual grounding involves matching natural language descriptions with their corresponding objects in 3D spaces. Existing methods often face challenges with accuracy in object recognition and struggle in interpreting complex linguistic queries, particularly with descriptions that involve multiple anchors or are view-dependent. In response, we present the MiKASA (Multi-Key-Anchor Scene-Aware) Transformer. Our novel end-to-end trained model integrates a self-attention-based scene-aware object encoder and an original multi-key-anchor technique, enhancing object recognition accuracy and the understanding of spatial relationships. Furthermore, MiKASA improves the explainability of decision-making, facilitating error diagnosis. Our model achieves the highest overall accuracy in the Referit3D challenge for both the Sr3D and Nr3D datasets, particularly excelling by a large margin in categories that require viewpoint-dependent descriptions. |
| title | MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.03077 |