Summary of Scene-llm: Extending Language Model For 3d Visual Understanding and Reasoning, by Rao Fu et al.
Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning
by Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie, Wenhan Xiong
First submitted to arxiv on: 18 Mar 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 Scene-LLM is a 3D-visual-language model that improves embodied agents’ abilities in interactive 3D indoor environments by combining Large Language Models (LLMs) with dense spatial information. The model uses a hybrid feature representation, projecting 3D visual features into the pre-trained textual embedding space to enable effective interpretation. It integrates scene-level and ego-centric 3D information for global planning and localization. Notably, it employs an efficient technique for aligning small object features within scenes. Experiments demonstrate Scene-LLM’s capabilities in dense captioning, question answering, and interactive planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scene-LLM is a new way to help robots understand and interact with their surroundings. It uses big language models to make decisions and takes into account what the robot sees around it. This helps the robot plan its actions better and understand its environment more accurately. The model can do tasks like describing what’s in a room, answering questions about what it sees, and even planning how to move around the space. |
Keywords
» Artificial intelligence » Embedding space » Language model » Question answering
