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

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Foundation models and large language models (LLMs) show promise for 3D scene understanding.
• Traditional 3D point cloud reconstruction methods suffer from information loss, especially with textureless surfaces and complex objects.
• Existing methods struggle with voids and distortions in reconstructed 3D data.

Purpose of the Study:

• To introduce Argus, a novel 3D multimodal framework for enhanced 3D scene understanding.
• To leverage 2D multiview images to compensate for deficiencies in 3D point cloud reconstruction.
• To expand the capabilities of LLMs in tackling complex 3D tasks through multimodal integration.

Main Methods:

• Developed Argus, a 3D large multimodal foundation model (3D-LMM) accepting text, 2D images, and 3D point clouds.
• Fused multiview images and camera poses into view-as-scene features.
• Integrated these features with 3D data to create detailed 3D-aware scene embeddings.

Main Results:

• Argus successfully compensates for information loss during 3D point cloud reconstruction.
• The framework enables LLMs to achieve a more comprehensive understanding of 3D scenes.
• Experimental results show superior performance compared to existing 3D-LMMs on various downstream tasks.

Conclusions:

• Argus offers a robust solution for improving 3D scene understanding by combining 2D and 3D data modalities.
• The proposed 3D-LMM architecture enhances the ability of LLMs to interpret and process complex 3D environments.
• This multimodal approach represents a significant advancement in the field of 3D artificial intelligence.