Summary of Grounding 3d Scene Affordance From Egocentric Interactions, by Cuiyu Liu et al.
Grounding 3D Scene Affordance From Egocentric Interactions
by Cuiyu Liu, Wei Zhai, Yuhang Yang, Hongchen Luo, Sen Liang, Yang Cao, Zheng-Jun Zha
First submitted to arxiv on: 29 Sep 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 proposes a novel task in grounding 3D scene affordance, enabling embodied agents to interact intelligently with their surroundings. The Egocentric Interaction-driven 3D Scene Affordance Grounding (Ego-SAG) framework is introduced to identify corresponding affordance regions in a 3D scene based on an egocentric video of an interaction. The task faces challenges of spatial complexity and alignment complexity across multiple sources, which are addressed by utilizing interaction intent to guide the model’s focus on interaction-relevant sub-regions and aligning affordance features through a bidirectional query decoder mechanism. The Egocentric Video-3D Scene Affordance Dataset (VSAD) is also introduced, covering common interaction types and diverse 3D environments. Extensive experiments validate both the feasibility of the task and the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines understand how humans interact with their environment. It’s like teaching a robot to play with toys or pick up objects. Most robots just look at what something looks like, but this paper shows how to teach them to learn by watching others do things. The goal is to make robots better at understanding and imitating human actions. To achieve this, the paper proposes a new way of analyzing videos of humans interacting with their surroundings and identifying which parts are important for interaction. |
Keywords
» Artificial intelligence » Alignment » Decoder » Grounding
