Summary of Scanreason: Empowering 3d Visual Grounding with Reasoning Capabilities, by Chenming Zhu et al.
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities
by Chenming Zhu, Tai Wang, Wenwei Zhang, Kai Chen, Xihui Liu
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 new task called 3D reasoning grounding, which involves reasoning human intentions from implicit instructions without explicit textual descriptions. The authors introduce ScanReason, a new benchmark with over 10K question-answer-location pairs from five reasoning types that require the synergy of reasoning and grounding. To tackle this challenge, they design ReGround3D, an approach composed of visual-centric reasoning module empowered by Multi-modal Large Language Model (MLLM) and 3D grounding module to obtain accurate object locations by leveraging enhanced geometry and fine-grained details from 3D scenes. A chain-of-grounding mechanism is proposed to further improve performance through interleaved reasoning and grounding steps during inference. The authors validate the effectiveness of their approach with extensive experiments on the proposed benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at understanding what we want them to do, just by looking at a 3D scene. Right now, computers need us to tell them exactly how to do things, but this new task lets them figure it out themselves from clues in the picture. The authors create a special set of questions and answers called ScanReason that helps test these computer skills. They also design a way for computers to reason about what they see and then use that to find specific objects in the scene. This approach is tested on a big dataset and shows promising results. |
Keywords
» Artificial intelligence » Grounding » Inference » Large language model » Multi modal
