LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning

TL;DR

LL3DA is a novel large language model-based assistant that directly processes point cloud data for improved understanding, reasoning, and planning in complex 3D environments, surpassing existing models in 3D vision-language tasks.

Contribution

The paper introduces LL3DA, a new approach that directly uses point cloud data for multimodal 3D understanding, reducing computational overhead and enhancing performance.

Findings

Outperforms existing 3D vision-language models in dense captioning.

Achieves superior results in 3D question answering.

Effectively handles complex and cluttered 3D scenes.

Abstract

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.