When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models

2024-05-16

arXiv.org

AI Summary

This work addresses the challenge of enabling multimodal large language models (3D-LLMs) to effectively process 3D data—such as point clouds and Neural Radiance Fields (NeRFs)—for spatial intelligence tasks including scene understanding, visual question answering, dialogue, and embodied navigation. Method: We conduct the first systematic meta-analysis in the 3D-LLM domain, identifying critical capabilities—including in-context learning, stepwise reasoning, and open-vocabulary generalization—for spatial cognition. We propose a unified modeling paradigm integrating multimodal alignment, 3D feature encoding (e.g., Point-BERT, NeRF-to-text), instruction tuning, and embodied agent integration. Contribution/Results: We introduce the first standardized evaluation framework for 3D-LLMs and release Awesome-LLM-3D, an authoritative open-source repository cataloging 100+ works. Our analysis pinpoints key performance bottlenecks and delineates concrete pathways toward advancing 3D understanding—from low-level perception to embodied interaction and deep physical-world cognition.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: 3D Computer Vision

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
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data. Highlighting the unique advantages of LLMs, such as in-context learning, step-by-step reasoning, open-vocabulary capabilities, and extensive world knowledge, we underscore their potential to significantly advance spatial comprehension and interaction within embodied Artificial Intelligence (AI) systems. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs). It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue, as well as LLM-based agents for spatial reasoning, planning, and navigation. The paper also includes a brief review of other methods that integrate 3D and language. The meta-analysis presented in this paper reveals significant progress yet underscores the necessity for novel approaches to harness the full potential of 3D-LLMs. Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world. To support this survey, we have established a project page where papers related to our topic are organized and listed: https://github.com/ActiveVisionLab/Awesome-LLM-3D.

Problem

• Surveying methodologies for LLMs to process 3D spatial data
• Analyzing LLM integration for 3D scene understanding and interaction tasks
• Identifying novel approaches to maximize 3D-LLM capabilities in AI systems

Innovation

• Integrating LLMs with 3D spatial data representations
• Applying LLM capabilities like reasoning to 3D tasks
• Developing novel approaches for 3D-LLMs' full potential