Empowering Large Language Models with 3D Situation Awareness

2025-03-29

AI Summary

Existing LLM-based 3D scene understanding methods rely on global-view data and neglect the observer’s egocentric pose (position and orientation), leading to spatial ambiguities such as left-right confusion. To address this, we propose the first egocentric-perspective modeling framework for 3D situational awareness. Our approach comprises three key components: (1) a scan-trajectory-driven situational data synthesis method that automatically generates high-quality multimodal training data with precise egocentric pose annotations; (2) an explicit situational localization module enabling LLMs to perform spatial grounding of “self-perspective” in 3D scenes; and (3) a unified architecture integrating vision-language models, pose prediction, and multimodal prompt alignment. Evaluated on multiple 3D understanding benchmarks, our framework achieves significant performance gains. Moreover, its fully automated data synthesis pipeline substantially reduces manual annotation effort and cost.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: 3D Computer Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Driven by the great success of Large Language Models (LLMs) in the 2D image domain, their applications in 3D scene understanding has emerged as a new trend. A key difference between 3D and 2D is that the situation of an egocentric observer in 3D scenes can change, resulting in different descriptions (e.g., ''left"or ''right"). However, current LLM-based methods overlook the egocentric perspective and simply use datasets from a global viewpoint. To address this issue, we propose a novel approach to automatically generate a situation-aware dataset by leveraging the scanning trajectory during data collection and utilizing Vision-Language Models (VLMs) to produce high-quality captions and question-answer pairs. Furthermore, we introduce a situation grounding module to explicitly predict the position and orientation of observer's viewpoint, thereby enabling LLMs to ground situation description in 3D scenes. We evaluate our approach on several benchmarks, demonstrating that our method effectively enhances the 3D situational awareness of LLMs while significantly expanding existing datasets and reducing manual effort.

Problem

• Enhancing LLMs' 3D scene understanding with egocentric perspective
• Automating situation-aware dataset generation for 3D scenes
• Improving viewpoint grounding in 3D descriptions for LLMs

Innovation

• Generates situation-aware dataset using scanning trajectory
• Employs Vision-Language Models for caption and QA pairs
• Introduces grounding module for viewpoint position prediction