# Point-E: A System for Generating 3D Point Clouds from Complex Prompts

**Authors:** Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen  
**arXiv:** [2212.08751](https://arxiv.org/abs/2212.08751)  
**Date:** 2022-12-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2212.08751v1/

## Key points

- The paper explores an alternative method for 3D object generation that produces 3D models in only 1-2 minutes on a single GPU.
- State-of-the-art methods typically require multiple GPU-hours to produce a single sample.
- The proposed method first generates a single synthetic view using a text-to-image diffusion model and then produces a 3D point cloud using a second diffusion model that conditions on the generated image.
- The authors release their pre-trained point cloud diffusion models as well as evaluation code and models at https://github.com/openai/pointe.
- Although the proposed method falls short of the state of the art in terms of sample quality, it is one to two orders of magnitude faster to sample from than existing methods.
- The authors note that while recent work has focused on generating complex 3D objects from textual prompts, there are still significant challenges in this area due to the high dimensionality of the problem space.
- However they believe that their approach represents an important step forward in addressing these challenges and could pave the way for further advances in text conditional 3D object generation.
- "Point E" presents an innovative solution to the challenge of generating complex 3D objects from textual prompts with significantly reduced computational requirements compared to existing methods.
- The pre trained models and evaluation code released by the authors provide valuable resources for researchers working in this field and offer potential applications across various industries including gaming virtual reality and product design.

## Summary

The paper "Point-E: A System for Generating 3D Point Clouds from Complex Prompts" by Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen explores an alternative method for 3D object generation that produces 3D models in only 1-2 minutes on a single GPU. While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models which produce samples in a number of seconds or minutes. The proposed method first generates a single synthetic view using a text-to-image diffusion model and then produces a 3D point cloud using a second diffusion model that conditions on the generated image. The authors release their pre-trained point cloud diffusion models as well as evaluation code and models at https://github.com/openai/pointe. Although the proposed method falls short of the state of the art in terms of sample quality, it is one to two orders of magnitude faster to sample from than existing methods. This offers a practical tradeoff for some use cases where speed is more important than sample quality. The authors note that while recent work has focused on generating complex 3D objects from textual prompts, there are still significant challenges in this area due to the high dimensionality of the problem space. However they believe that their approach represents an important step forward in addressing these challenges and could pave the way for further advances in text conditional 3D object generation. Overall "Point E" presents an innovative solution to the challenge of generating complex 3D objects from textual prompts with significantly reduced computational requirements compared to existing methods. The pre trained models and evaluation code released by the authors provide valuable resources for researchers working in this field and offer potential applications across various industries including gaming virtual reality and product design.