| | --- |
| | license: mit |
| | language: |
| | - en |
| | --- |
| | # Point·E |
| |
|
| |  |
| |
|
| | 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). |
