Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with
  Large Language Models
Dingning Liu Xiaoshui Huang Yuenan Hou Zhihui Wang Zhenfei Yin Yongshun Gong Peng Gao Wanli Ouyang
In this paper, we introduce Uni3D-LLM, a unified framework that leverages a
Large Language Model (LLM) to integrate tasks of 3D perception, generation, and
editing within point cloud scenes. This framework empowers users to
effortlessly generate and modify objects at specified locations within a scene,
guided by the versatility of natural language descriptions. Uni3D-LLM harnesses
the expressive power of natural language to allow for precise command over the
generation and editing of 3D objects, thereby significantly enhancing
operational flexibility and controllability. By mapping point cloud into the
unified representation space, Uni3D-LLM achieves cross-application
functionality, enabling the seamless execution of a wide array of tasks,
ranging from the accurate instantiation of 3D objects to the diverse
requirements of interactive design. Through a comprehensive suite of rigorous
experiments, the efficacy of Uni3D-LLM in the comprehension, generation, and
editing of point cloud has been validated. Additionally, we have assessed the
impact of integrating a point cloud perception module on the generation and
editing processes, confirming the substantial potential of our approach for
practical applications.