Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning

• URL: http://arxiv.org/abs/2403.11401v2
• Date: Fri, 22 Mar 2024 18:52:51 GMT
• Title: Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning
• Authors: Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie, Wenhan Xiong,
• Abstract summary: Scene-LLM is a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments. Our experiments with Scene-LLM demonstrate its strong capabilities in dense captioning, question answering, and interactive planning.
• Score: 24.162598399141785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces Scene-LLM, a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments by integrating the reasoning strengths of Large Language Models (LLMs). Scene-LLM adopts a hybrid 3D visual feature representation, that incorporates dense spatial information and supports scene state updates. The model employs a projection layer to efficiently project these features in the pre-trained textual embedding space, enabling effective interpretation of 3D visual information. Unique to our approach is the integration of both scene-level and ego-centric 3D information. This combination is pivotal for interactive planning, where scene-level data supports global planning and ego-centric data is important for localization. Notably, we use ego-centric 3D frame features for feature alignment, an efficient technique that enhances the model's ability to align features of small objects within the scene. Our experiments with Scene-LLM demonstrate its strong capabilities in dense captioning, question answering, and interactive planning. We believe Scene-LLM advances the field of 3D visual understanding and reasoning, offering new possibilities for sophisticated agent interactions in indoor settings.

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