Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913807592325120 |
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| author | Li, Maximilian Xiling Rudolf, Korbinian Blank, Nils Lioutikov, Rudolf |
| author_facet | Li, Maximilian Xiling Rudolf, Korbinian Blank, Nils Lioutikov, Rudolf |
| contents | Robotic agents need to understand how to interact with objects in their environment, both autonomously and during human-robot interactions. Affordance detection on 3D point clouds, which identifies object regions that allow specific interactions, has traditionally relied on deep learning models like PointNet++, DGCNN, or PointTransformerV3. However, these models operate as black boxes, offering no insight into their decision-making processes. Prototypical Learning methods, such as ProtoPNet, provide an interpretable alternative to black-box models by employing a "this looks like that" case-based reasoning approach. However, they have been primarily applied to image-based tasks. In this work, we apply prototypical learning to models for affordance detection on 3D point clouds. Experiments on the 3D-AffordanceNet benchmark dataset show that prototypical models achieve competitive performance with state-of-the-art black-box models and offer inherent interpretability. This makes prototypical models a promising candidate for human-robot interaction scenarios that require increased trust and safety. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_18355 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes Li, Maximilian Xiling Rudolf, Korbinian Blank, Nils Lioutikov, Rudolf Computer Vision and Pattern Recognition Robotics Robotic agents need to understand how to interact with objects in their environment, both autonomously and during human-robot interactions. Affordance detection on 3D point clouds, which identifies object regions that allow specific interactions, has traditionally relied on deep learning models like PointNet++, DGCNN, or PointTransformerV3. However, these models operate as black boxes, offering no insight into their decision-making processes. Prototypical Learning methods, such as ProtoPNet, provide an interpretable alternative to black-box models by employing a "this looks like that" case-based reasoning approach. However, they have been primarily applied to image-based tasks. In this work, we apply prototypical learning to models for affordance detection on 3D point clouds. Experiments on the 3D-AffordanceNet benchmark dataset show that prototypical models achieve competitive performance with state-of-the-art black-box models and offer inherent interpretability. This makes prototypical models a promising candidate for human-robot interaction scenarios that require increased trust and safety. |
| title | Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2504.18355 |