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point-e

(★ 6,892)

Point cloud diffusion for 3D model synthesis

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Download Latest Version (.zip)

- - __init__.py
  - configs.py
  - gaussian_diffusion.py
  - k_diffusion.py
  - sampler.py
- - __init__.py
  - feature_extractor.py
  - fid_is.py
  - npz_stream.py
  - pointnet2_cls_ssg.py
  - pointnet2_utils.py
- - image2pointcloud.ipynb
  - paper_banner.gif
  - pointcloud2mesh.ipynb
  - text2pointcloud.ipynb
- - __init__.py
  - checkpoint.py
  - configs.py
  - download.py
  - perceiver.py
  - pretrained_clip.py
  - sdf.py
  - transformer.py
  - util.py
- - __init__.py
  - mesh.py
  - pc_to_mesh.py
  - plotting.py
  - ply_util.py
  - point_cloud.py
- __init__.py
.gitignore
LICENSE
model-card.md
README.md
setup.py

🚀 Installation Guide

1. Get the code

git clone https://github.com/openai/point-e

Downloads the entire project code from GitHub to your computer.

cd point-e

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install .

Installs the package published on PyPI directly — no need to clone the source.

jupyter notebook

Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# Point·E

![Animation of four 3D point clouds rotating](https://raw.githubusercontent.com/openai/point-e/main/point_e/examples/paper_banner.gif)

This is the official code and model release for [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://arxiv.org/abs/2212.08751).

# Usage

Install with `pip install -e .`.

To get started with examples, see the following notebooks:

* [image2pointcloud.ipynb](point_e/examples/image2pointcloud.ipynb) - sample a point cloud, conditioned on some example synthetic view images.
 * [text2pointcloud.ipynb](point_e/examples/text2pointcloud.ipynb) - use our small, worse quality pure text-to-3D model to produce 3D point clouds directly from text descriptions. This model's capabilities are limited, but it does understand some simple categories and colors.
 * [pointcloud2mesh.ipynb](point_e/examples/pointcloud2mesh.ipynb) - try our SDF regression model for producing meshes from point clouds.

For our P-FID and P-IS evaluation scripts, see:

* [evaluate_pfid.py](point_e/evals/scripts/evaluate_pfid.py)
 * [evaluate_pis.py](point_e/evals/scripts/evaluate_pis.py)

For our Blender rendering code, see [blender_script.py](point_e/evals/scripts/blender_script.py)

# Samples

You can download the seed images and point clouds corresponding to the paper banner images [here](https://openaipublic.azureedge.net/main/point-e/banner_pcs.zip).

You can download the seed images used for COCO CLIP R-Precision evaluations [here](https://openaipublic.azureedge.net/main/point-e/coco_images.zip).