Argus: Leveraging Multiview Images for Improved 3-D Scene Understanding With Large Language Models

2025-07-17

AI Summary

To address the challenges of texture scarcity, overlooked repetitive structures, and image–point-cloud misalignment causing detail distortion in 3D point cloud-based indoor scene understanding, this work proposes the first 3D multimodal large language model (MLLM) framework that jointly models multi-view RGB images and camera poses as “View-as-Scene” features. Taking multi-view RGB images, 3D point clouds, and textual instructions as input, the method employs a vision encoder co-optimized with a large language model to achieve cross-modal alignment and fine-grained feature fusion. Its core innovation lies in explicitly encoding geometric priors of camera viewpoints, thereby bridging the semantic gap between 2D visual representations and 3D structural semantics. Evaluated on downstream tasks—including 3D visual question answering, scene captioning, and instruction-driven reasoning—the framework significantly outperforms existing 3D multimodal LLMs, demonstrating superior effectiveness and generalizability for complex indoor scene understanding.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Advancements in foundation models have made it possible to conduct applications in various downstream tasks. Especially, the new era has witnessed a remarkable capability to extend Large Language Models (LLMs) for tackling tasks of 3D scene understanding. Current methods rely heavily on 3D point clouds, but the 3D point cloud reconstruction of an indoor scene often results in information loss. Some textureless planes or repetitive patterns are prone to omission and manifest as voids within the reconstructed 3D point clouds. Besides, objects with complex structures tend to introduce distortion of details caused by misalignments between the captured images and the dense reconstructed point clouds. 2D multi-view images present visual consistency with 3D point clouds and provide more detailed representations of scene components, which can naturally compensate for these deficiencies. Based on these insights, we propose Argus, a novel 3D multimodal framework that leverages multi-view images for enhanced 3D scene understanding with LLMs. In general, Argus can be treated as a 3D Large Multimodal Foundation Model (3D-LMM) since it takes various modalities as input(text instructions, 2D multi-view images, and 3D point clouds) and expands the capability of LLMs to tackle 3D tasks. Argus involves fusing and integrating multi-view images and camera poses into view-as-scene features, which interact with the 3D features to create comprehensive and detailed 3D-aware scene embeddings. Our approach compensates for the information loss while reconstructing 3D point clouds and helps LLMs better understand the 3D world. Extensive experiments demonstrate that our method outperforms existing 3D-LMMs in various downstream tasks.

Problem

• Addressing information loss in 3D point cloud reconstruction
• Compensating for textureless plane omissions in 3D scenes
• Enhancing 3D scene understanding with multi-view images

Innovation

• Leverages multi-view images for 3D scene understanding
• Fuses text, 2D images, and 3D point clouds
• Compensates 3D point cloud information loss