CompassAD: Intent-Driven 3D Affordance Grounding in Functionally Competing Objects
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913139419774976 |
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| author | Li, Jingliang Jia, Jindou An, Tuo Zhou, Chuhao Chen, Xiangyu Shan, Shilin Ma, Boyu Lyu, Bofan Li, Gen Yang, Jianfei |
| author_facet | Li, Jingliang Jia, Jindou An, Tuo Zhou, Chuhao Chen, Xiangyu Shan, Shilin Ma, Boyu Lyu, Bofan Li, Gen Yang, Jianfei |
| contents | When told to "cut the cake," a robot must choose the knife over nearby scissors, despite both objects affording the same cutting function. In real-world scenes, multiple objects may share identical affordances, yet only one is appropriate under the given task context. We call such cases confusing pairs. However, existing 3D affordance methods largely sidestep this challenge by evaluating isolated single objects, often with explicit category names provided in the query. We formalize Intent-Driven Confusable Affordance Grounding, a new 3D affordance setting that requires predicting a per-point affordance mask on the correct object within a multi-object point cloud, conditioned on implicit natural language intent. To study this problem, we construct CompassAD, the first benchmark centered on implicit intent in confusing multi-object compositions. It comprises 30 confusing object pairs spanning 16 affordance types, 6,422 compositions, and 88K+ query-answer pairs. Furthermore, we propose CompassNet, a framework that incorporates two dedicated modules tailored to this task. Instance-bounded Cross Injection (ICI) constrains language-geometry alignment within object boundaries to prevent cross-object semantic leakage. Bi-level Contrastive Refinement (BCR) enforces discrimination at both geometric-group and point levels, sharpening distinctions between target and confusable surfaces. Extensive experiments demonstrate state-of-the-art results on both seen and unseen queries, and deployment on a robotic manipulator confirms effective transfer to real-world grasping in confusing multi-object compositions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02060 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CompassAD: Intent-Driven 3D Affordance Grounding in Functionally Competing Objects Li, Jingliang Jia, Jindou An, Tuo Zhou, Chuhao Chen, Xiangyu Shan, Shilin Ma, Boyu Lyu, Bofan Li, Gen Yang, Jianfei Computer Vision and Pattern Recognition Robotics When told to "cut the cake," a robot must choose the knife over nearby scissors, despite both objects affording the same cutting function. In real-world scenes, multiple objects may share identical affordances, yet only one is appropriate under the given task context. We call such cases confusing pairs. However, existing 3D affordance methods largely sidestep this challenge by evaluating isolated single objects, often with explicit category names provided in the query. We formalize Intent-Driven Confusable Affordance Grounding, a new 3D affordance setting that requires predicting a per-point affordance mask on the correct object within a multi-object point cloud, conditioned on implicit natural language intent. To study this problem, we construct CompassAD, the first benchmark centered on implicit intent in confusing multi-object compositions. It comprises 30 confusing object pairs spanning 16 affordance types, 6,422 compositions, and 88K+ query-answer pairs. Furthermore, we propose CompassNet, a framework that incorporates two dedicated modules tailored to this task. Instance-bounded Cross Injection (ICI) constrains language-geometry alignment within object boundaries to prevent cross-object semantic leakage. Bi-level Contrastive Refinement (BCR) enforces discrimination at both geometric-group and point levels, sharpening distinctions between target and confusable surfaces. Extensive experiments demonstrate state-of-the-art results on both seen and unseen queries, and deployment on a robotic manipulator confirms effective transfer to real-world grasping in confusing multi-object compositions. |
| title | CompassAD: Intent-Driven 3D Affordance Grounding in Functionally Competing Objects |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2604.02060 |