title: GPT4Point: A Unified Framework for Point-Language Understanding and
    Generation
summary: Multimodal Large Language Models (MLLMs) have excelled in 2D image-text
  comprehension and image generation, but their understanding of the 3D world is
  notably deficient, limiting progress in 3D language understanding and
  generation. To solve this problem, we introduce GPT4Point, an innovative
  groundbreaking point-language multimodal model designed specifically for
  unified 3D object understanding and generation within the MLLM framework.
  GPT4Point as a powerful 3D MLLM seamlessly can execute a variety of point-text
  reference tasks such as point-cloud captioning and Q&A. Additionally, GPT4Point
  is equipped with advanced capabilities for controllable 3D generation, it can
  get high-quality results through a low-quality point-text feature maintaining
  the geometric shapes and colors. To support the expansive needs of 3D
  object-text pairs, we develop Pyramid-XL, a point-language dataset annotation
  engine. It constructs a large-scale database over 1M objects of varied text
  granularity levels from the Objaverse-XL dataset, essential for training
  GPT4Point. A comprehensive benchmark has been proposed to evaluate 3D
  point-language understanding capabilities. In extensive evaluations, GPT4Point
  has demonstrated superior performance in understanding and generation.
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paper_authors.name: Zhangyang Qi
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paper_authors.name: Ye Fang
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paper_authors.name: Zeyi Sun
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paper_authors.fullname: Zeyi Sun
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paper_authors.name: Xiaoyang Wu
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paper_authors.fullname: Xiaoyang Wu
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paper_authors.name: Tong Wu
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paper_authors.fullname: Tong Wu
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paper_authors.user: wutong16
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paper_authors.name: Jiaqi Wang
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paper_authors.fullname: Jiaqi Wang
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paper_authors.user: myownskyW7
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paper_authors.name: Dahua Lin
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paper_authors.fullname: Dahua Lin
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paper_authors.name: Hengshuang Zhao
paper_title: GPT4Point: A Unified Framework for Point-Language Understanding and
    Generation
paper_summary: Multimodal Large Language Models (MLLMs) have excelled in 2D image-text
  comprehension and image generation, but their understanding of the 3D world is
  notably deficient, limiting progress in 3D language understanding and
  generation. To solve this problem, we introduce GPT4Point, an innovative
  groundbreaking point-language multimodal model designed specifically for
  unified 3D object understanding and generation within the MLLM framework.
  GPT4Point as a powerful 3D MLLM seamlessly can execute a variety of point-text
  reference tasks such as point-cloud captioning and Q&A. Additionally, GPT4Point
  is equipped with advanced capabilities for controllable 3D generation, it can
  get high-quality results through a low-quality point-text feature maintaining
  the geometric shapes and colors. To support the expansive needs of 3D
  object-text pairs, we develop Pyramid-XL, a point-language dataset annotation
  engine. It constructs a large-scale database over 1M objects of varied text
  granularity levels from the Objaverse-XL dataset, essential for training
  GPT4Point. A comprehensive benchmark has been proposed to evaluate 3D
  point-language understanding capabilities. In extensive evaluations, GPT4Point
  has demonstrated superior performance in understanding and generation.
paper_ai_keywords: Multimodal Large Language Models (MLLMs) point-language multimodal model 3D object understanding 3D generation point-cloud captioning Q&A controllable 3D generation point-text feature neural shapes colors Pyramid-XL point-language dataset annotation engine Objaverse-XL dataset 3D point-language understanding capabilities