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point-e

(★ 6,892)

Point cloud diffusion for 3D model synthesis

파일 탐색기

최종 버전 다운로드 (.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

# 설치 가이드

1. 코드 내려받기

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

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd point-e

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// 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).