JM3D & JM3D-LLM: Elevating 3D Representation With Joint Multi-Modal Cues.

Area of Science:

• Computer Vision
• Machine Learning
• Robotics

Background:

• 3D representation learning is crucial for computer vision, autonomous driving, and robotics.
• Existing 2D alignment strategies face challenges like information degradation, insufficient synergy, and underutilization when applied to 3D data.

Purpose of the Study:

• To introduce JM3D, a novel approach for 3D representation learning that addresses the limitations of current methods.
• To improve the integration of 3D data with visual and textual information.

Main Methods:

• Developed the Structured Multimodal Organizer (SMO) for enriching vision-language representation with multi-view images and hierarchical text.
• Implemented Joint Multi-modal Alignment (JMA) to combine language understanding with visual representation.
• Introduced JM3D-LLM, integrating 3D representation with large language models through efficient fine-tuning.

Main Results:

• JM3D demonstrates superior performance compared to existing methods on benchmark datasets like ModelNet40 and ScanObjectNN.
• The JM3D-LLM model shows significant effectiveness in representation transfer, leveraging large language models.

Conclusions:

• JM3D offers a comprehensive solution for 3D representation learning by effectively integrating multimodal information.
• The proposed approach successfully overcomes the challenges associated with transferring 2D alignment strategies to the 3D domain, paving the way for more robust 3D understanding.