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GPT4Point++: Advancing Unified Point-Language Understanding and Generation.

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    GPT4Point and GPT4Point++ are new multimodal large language models for 3D understanding and generation. These models advance 3D object recognition and controllable 3D generation, supported by the Capverse dataset.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Multimodal Large Language Models (MLLMs) show promise in 2D tasks but struggle with 3D data.
    • The 3D domain requires specialized models for object understanding and generation.

    Purpose of the Study:

    • Introduce GPT4Point and GPT4Point++, pioneering point-language multimodal models for 3D tasks.
    • Address the challenge of 3D object understanding and controllable 3D generation.
    • Develop a large-scale 3D point-language dataset and benchmark.

    Main Methods:

    • GPT4Point uses a two-stage training: point-text feature alignment followed by LLM integration.
    • GPT4Point++ employs a unified, end-to-end training approach for enhanced performance.
    • Capverse, a novel annotation engine, constructs a large-scale 3D point-language dataset from Objaverse.
    • A comprehensive benchmark is established for evaluating 3D point-language understanding.

    Main Results:

    • GPT4Point and GPT4Point++ demonstrate strong performance in 3D object recognition, captioning, and question answering.
    • GPT4Point enables controllable 3D generation, maintaining geometric and color fidelity from low-quality inputs.
    • The models show robustness in evaluating 3D generation methods and understanding complex scenes.

    Conclusions:

    • GPT4Point and GPT4Point++ represent significant advancements in 3D multimodal AI.
    • The developed dataset and benchmark facilitate future research in 3D point-language understanding.
    • These models offer versatile capabilities for both 3D object understanding and generation tasks.