Chat-Scene++: Exploiting Context-Rich Object Identification for 3D LLM.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Natural Language Processing

Background:

• Multi-modal large language models (MLLMs) show promise for 3D scene understanding.
• Existing methods face challenges in fine-grained object grounding and contextual reasoning within complex 3D environments.

Purpose of the Study:

• To introduce Chat-Scene++, a novel MLLM framework for advanced 3D scene understanding.
• To enable object-centric representation and interaction within 3D scenes by structuring them as context-rich object sequences.

Main Methods:

• Representing 3D scenes as sequences of objects with contextual semantics and identifier tokens.
• Extracting context-rich object features using large-scale pre-trained 3D scene-level and 2D image-level encoders.
• Implementing grounded chain-of-thought (G-CoT) reasoning for multi-step inference at category and spatial levels.

Main Results:

• Achieved state-of-the-art performance on five major 3D vision-language benchmarks (ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, SQA3D).
• Demonstrated effectiveness in scene comprehension, object grounding, and spatial reasoning without task-specific heads or fine-tuning.
• Showcased applicability to real-world scenarios using only 2D inputs, avoiding computationally expensive 3D reconstruction.

Conclusions:

• Chat-Scene++ offers a robust and efficient approach to 3D scene understanding and interaction.
• The object-centric and context-aware design significantly advances vision-language tasks.
• The framework's flexibility and performance highlight its potential for real-world applications.