Summary of Great: Geometry-intention Collaborative Inference For Open-vocabulary 3d Object Affordance Grounding, by Yawen Shao et al.
GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding
by Yawen Shao, Wei Zhai, Yuhang Yang, Hongchen Luo, Yang Cao, Zheng-Jun Zha
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework called GREAT for Open-Vocabulary 3D Object Affordance Grounding, which enables robots to anticipate potential interactions with arbitrary instructions. The existing methods rely on combining images or languages with 3D geometries, but they are limited by their semantic space and lack of consideration for implied invariant geometries and potential interaction intentions. GREAT addresses this limitation by mining object invariant geometry attributes and performing analogical reasoning in potential interaction scenarios to form affordance knowledge. The proposed framework combines this knowledge with both geometries and visual contents to ground 3D object affordance. To support the task, the paper introduces the Point Image Affordance Dataset v2 (PIADv2), the largest 3D object affordance dataset available. Experimental results demonstrate the effectiveness and superiority of GREAT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots understand what they can do with objects. Right now, robots are not very good at this because they don’t consider all the possible ways humans might interact with an object. The authors propose a new way for robots to learn about these interactions by looking at the shape and structure of an object. They also provide a large dataset of examples that robots can use to practice this skill. |
Keywords
» Artificial intelligence » Grounding
