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license: mit |
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language: |
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- en |
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# Point·E |
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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). |
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# Usage |
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Install with `pip install -e .`. |
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To get started with examples, see the following notebooks: |
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* [image2pointcloud.ipynb](point_e/examples/image2pointcloud.ipynb) - sample a point cloud, conditioned on some example synthetic view images. |
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* [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. |
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* [pointcloud2mesh.ipynb](point_e/examples/pointcloud2mesh.ipynb) - try our SDF regression model for producing meshes from point clouds. |
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For our P-FID and P-IS evaluation scripts, see: |
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* [evaluate_pfid.py](point_e/evals/scripts/evaluate_pfid.py) |
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* [evaluate_pis.py](point_e/evals/scripts/evaluate_pis.py) |
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For our Blender rendering code, see [blender_script.py](point_e/evals/scripts/blender_script.py) |
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# Samples |
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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). |
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You can download the seed images used for COCO CLIP R-Precision evaluations [here](https://openaipublic.azureedge.net/main/point-e/coco_images.zip). |
