GPT4Point: A Unified Framework for Point-Language Understanding and Generation

Zhangyang Qi, Ye Fang, Zeyi Sun, Xiaoyang Wu, Tong Wu, Jiaqi Wang, Dahua Lin, Hengshuang Zhao

Abstract: Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation. Still, their understanding of the 3D world needs to be improved, limiting progress in 3D language understanding and generation. To solve this problem, we introduce GPT4Point, an innovative, groundbreaking point-language multimodal model explicitly designed for unified 3D object understanding and generation within the MLLMframework. GPT4Point, as a powerful 3D MLLM, can seamlessly execute point-text reference tasks such as point-cloud captioning and Q&A. Additionally, GPT4Point is equipped with advanced capabilities for controllable 3D generation, and it can get high-quality results through a low-quality point-text feature that maintains geometric shapes and colors. We develop Pyramid-XL, a point-language dataset annotation engine, to support the expansive needs of 3D object-text pairs. It constructs a large-scale database of 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.