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Point cloud diffusion for 3D model synthesis

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Point·E

Animation of four 3D point clouds rotating

This is the official code and model release for Point-E: A System for Generating 3D Point Clouds from Complex Prompts.

Usage

Install with pip install -e ..

To get started with examples, see the following notebooks:

  • image2pointcloud.ipynb - sample a point cloud, conditioned on some example synthetic view images.
  • 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 - try our SDF regression model for producing meshes from point clouds.

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

For our Blender rendering code, see blender_script.py

Samples

You can download the seed images and point clouds corresponding to the paper banner images here.

You can download the seed images used for COCO CLIP R-Precision evaluations here.

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Point cloud diffusion for 3D model synthesis

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  • Python 95.1%
  • Jupyter Notebook 4.9%