Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D
  Understanding, Generation, and Instruction Following
Ziyu Guo Renrui Zhang Xiangyang Zhu Yiwen Tang Xianzheng Ma Jiaming Han Kexin Chen Peng Gao Xianzhi Li Hongsheng Li Pheng-Ann Heng
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with
2D image, language, audio, and video. Guided by ImageBind, we construct a joint
embedding space between 3D and multi-modalities, enabling many promising
applications, e.g., any-to-3D generation, 3D embedding arithmetic, and 3D
open-world understanding. On top of this, we further present Point-LLM, the
first 3D large language model (LLM) following 3D multi-modal instructions. By
parameter-efficient fine-tuning techniques, Point-LLM injects the semantics of
Point-Bind into pre-trained LLMs, e.g., LLaMA, which requires no 3D instruction
data, but exhibits superior 3D and multi-modal question-answering capacity. We
hope our work may cast a light on the community for extending 3D point clouds
to multi-modality applications. Code is available at
https://github.com/ZiyuGuo99/Point-Bind_Point-LLM.