Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning
<|reference_start|>Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning: 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.<|reference_end|>
@article{fu2024scene-llm:,
    title={Scene-LLM: Extending Language Model for 3D Visual Understanding and
  Reasoning},
    author={Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie, Wenhan Xiong},
    journal={arXiv preprint arXiv:2403.11401},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11401},
    primaryClass={cs.CV cs.AI}
}