AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914431456247808 |
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| author | Vu, Nghia Do, Tuong Nguyen, Khang Huang, Baoru Le, Nhat Nguyen, Binh Xuan Tjiputra, Erman Tran, Quang D. Prakash, Ravi Chiu, Te-Chuan Nguyen, Anh |
| author_facet | Vu, Nghia Do, Tuong Nguyen, Khang Huang, Baoru Le, Nhat Nguyen, Binh Xuan Tjiputra, Erman Tran, Quang D. Prakash, Ravi Chiu, Te-Chuan Nguyen, Anh |
| contents | Affordance learning is a complex challenge in many applications, where existing approaches primarily focus on the geometric structures, visual knowledge, and affordance labels of objects to determine interactable regions. However, extending this learning capability to a scene is significantly more complicated, as incorporating object- and scene-level semantics is not straightforward. In this work, we introduce AffordBridge, a large-scale dataset with 291,637 functional interaction annotations across 685 high-resolution indoor scenes in the form of point clouds. Our affordance annotations are complemented by RGB images that are linked to the same instances within the scenes. Building upon our dataset, we propose AffordMatcher, an affordance learning method that establishes coherent semantic correspondences between image-based and point cloud-based instances for keypoint matching, enabling a more precise identification of affordance regions based on cues, so-called visual signifiers. Experimental results on our dataset demonstrate the effectiveness of our approach compared to other methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_27970 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers Vu, Nghia Do, Tuong Nguyen, Khang Huang, Baoru Le, Nhat Nguyen, Binh Xuan Tjiputra, Erman Tran, Quang D. Prakash, Ravi Chiu, Te-Chuan Nguyen, Anh Computer Vision and Pattern Recognition Affordance learning is a complex challenge in many applications, where existing approaches primarily focus on the geometric structures, visual knowledge, and affordance labels of objects to determine interactable regions. However, extending this learning capability to a scene is significantly more complicated, as incorporating object- and scene-level semantics is not straightforward. In this work, we introduce AffordBridge, a large-scale dataset with 291,637 functional interaction annotations across 685 high-resolution indoor scenes in the form of point clouds. Our affordance annotations are complemented by RGB images that are linked to the same instances within the scenes. Building upon our dataset, we propose AffordMatcher, an affordance learning method that establishes coherent semantic correspondences between image-based and point cloud-based instances for keypoint matching, enabling a more precise identification of affordance regions based on cues, so-called visual signifiers. Experimental results on our dataset demonstrate the effectiveness of our approach compared to other methods. |
| title | AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.27970 |