LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding,
  Reasoning, and Planning
Sijin Chen Xin Chen Chi Zhang Mingsheng Li Gang Yu Hao Fei Hongyuan Zhu Jiayuan Fan Tao Chen
Recent advances in Large Multimodal Models (LMM) have made it possible for
various applications in human-machine interactions. However, developing LMMs
that can comprehend, reason, and plan in complex and diverse 3D environments
remains a challenging topic, especially considering the demand for
understanding permutation-invariant point cloud 3D representations of the 3D
scene. Existing works seek help from multi-view images, and project 2D features
to 3D space as 3D scene representations. This, however, leads to huge
computational overhead and performance degradation. In this paper, we present
LL3DA, a Large Language 3D Assistant that takes point cloud as direct input and
respond to both textual-instructions and visual-prompts. This help LMMs better
comprehend human interactions and further help to remove the ambiguities in
cluttered 3D scenes. Experiments show that LL3DA achieves remarkable results,
and surpasses various 3D vision-language models on both 3D Dense Captioning and
3D Question Answering.