# Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

**Authors:** Ziyu Guo, Renrui Zhang, Xiangyang Zhu, Yiwen Tang, Xianzheng Ma, Jiaming Han, Kexin Chen, Peng Gao, Xianzhi Li, Hongsheng Li, Pheng-Ann Heng  
**arXiv:** [2309.00615](https://arxiv.org/abs/2309.00615)  
**Date:** 2023-09-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.00615v1/

## Key points

- Introduces Point-Bind, a 3D multi-modality model that aligns point clouds with 2D images, language, audio, and video
- Utilizes ImageBind to construct a joint embedding space between 3D and multi-modalities
- Enables various applications such as any-to-3D generation, 3D embedding arithmetic, and 3D open-world understanding
- Presents Point-LLM, the first 3D large language model capable of following 3D multi-modal instructions
- Incorporates the semantics of Point-Bind into pre-trained LLMs like LLaMA using parameter-efficient fine-tuning techniques
- Exhibits superior performance in 3D and multi-modal question-answering tasks without requiring specific 3D instruction data
- Aims to extend the use of 3D point clouds in multi-modality applications

## Summary

, , , , 

The paper introduces Point-Bind, a 3D multi-modality model that aligns point clouds with 2D images, language, audio, and video. The authors utilize ImageBind to construct a joint embedding space between 3D and multi-modalities, enabling various applications such as any-to-3D generation, 3D embedding arithmetic, and 3D open-world understanding. Additionally, the paper presents Point-LLM, which is the first 3D large language model (LLM) capable of following 3D multi-modal instructions. By employing parameter-efficient fine-tuning techniques, Point-LLM incorporates the semantics of Point-Bind into pre-trained LLMs like LLaMA. Remarkably, Point-LLM exhibits superior performance in 3D and multi-modal question-answering tasks without requiring specific 3D instruction data. The authors hope that their work will contribute to extending the use of 3D point clouds in multi-modality applications. The code for this research is available at https://github.com/ZiyuGuo99/Point-Bind_Point-LLM.