MLLM-For3D introduces a framework that leverages existing 2D multimodal large language models to solve complex 3D reasoning segmentation tasks without expensive 3D training data.

• Creates multi-view pseudo segmentation masks from 2D models
• Projects 2D understanding into 3D space with spatial consistency
• Achieves effective 3D reasoning without specialized 3D training
• Demonstrates practical applications in engineering, construction and security contexts

This research enables more intelligent 3D scene understanding systems that can comprehend natural language instructions and reason about spatial relationships in complex environments—critical for advancing autonomous systems, robotics, and CAD/CAM applications.

MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation