LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning
This addresses the problem of efficient and accurate 3D scene understanding for human-machine interaction applications, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of enabling Large Multimodal Models to understand, reason, and plan in complex 3D environments by directly processing point clouds, avoiding the computational overhead and performance issues of multi-view image methods. It introduces LL3DA, which achieves remarkable results and surpasses existing models on 3D Dense Captioning and 3D Question Answering tasks.
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.