3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene Understanding

2025-01-14

AI Summary

Current large language models (LLMs) struggle to reason about spatial relationships, causal logic, and physical interactions in 3D scenes, primarily due to the scarcity of labeled 3D data, high annotation costs, and the computational complexity of end-to-end point cloud processing. To address this, we propose the first end-to-end 3D multimodal LLM (3D-MLLM), which directly accepts raw point clouds and textual instructions as input. Our contributions include: (1) a novel 3D compression module that jointly compresses spatial features and textual semantics; (2) an automated annotation pipeline built upon open-source 2D MLLMs, yielding the high-quality pretraining dataset 3DS-160K; and (3) a point cloud encoder–cross-modal fusion decoder architecture incorporating 3D feature projection and hybrid token compression. On ScanQA, our model achieves a 7.1% CIDEr improvement over state-of-the-art methods with reduced training overhead. The code, model weights, and dataset are fully open-sourced.

Technology Category

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

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

Multi-modal Large Language Models (MLLMs) exhibit impressive capabilities in 2D tasks, yet encounter challenges in discerning the spatial positions, interrelations, and causal logic in scenes when transitioning from 2D to 3D representations. We find that the limitations mainly lie in: i) the high annotation cost restricting the scale-up of volumes of 3D scene data, and ii) the lack of a straightforward and effective way to perceive 3D information which results in prolonged training durations and complicates the streamlined framework. To this end, we develop pipeline based on open-source 2D MLLMs and LLMs to generate high-quality 3D-text pairs and construct 3DS-160K , to enhance the pre-training process. Leveraging this high-quality pre-training data, we introduce the 3UR-LLM model, an end-to-end 3D MLLM designed for precise interpretation of 3D scenes, showcasing exceptional capability in navigating the complexities of the physical world. 3UR-LLM directly receives 3D point cloud as input and project 3D features fused with text instructions into a manageable set of tokens. Considering the computation burden derived from these hybrid tokens, we design a 3D compressor module to cohesively compress the 3D spatial cues and textual narrative. 3UR-LLM achieves promising performance with respect to the previous SOTAs, for instance, 3UR-LLM exceeds its counterparts by 7.1% CIDEr on ScanQA, while utilizing fewer training resources. The code and model weights for 3UR-LLM and the 3DS-160K benchmark are available at 3UR-LLM.

Problem

• 3D Image Understanding
• Large Language Models Limitations
• Stereo Image Data Complexity

Innovation

• 3D Scene Understanding
• 3UR-LLM Model
• 3DS-160K Dataset