readme: ---
  license: other
  
  tags:
  - diffusion
  - point-cloud
  - airplane
  - 3D
  
  datasets:
  - shapenet
  ---
  
  ### Model Description
  – Luo, Shitong and Hu, Wei
  – 2021
  
  Proposed a probabilistic generative model for point clouds inspired by non-equilibrium thermodynamics, exploiting the reverse diffusion process to learn the point distribution. All models are available on the original [***Github repo Link***](https://github.com/luost26/diffusion-point-cloud). It consists of a model for airplane model generating.
  
  
  ### Documents
  - [GitHub Repo](https://github.com/luost26/diffusion-point-cloud)
  - [Paper - Diffusion Probabilistic Models for 3D Point Cloud Generation](https://arxiv.org/abs/2103.01458)
    
  ### Datasets
  ShapeNet is a comprehensive 3D shape dataset created for research in computer graphics, computer vision, robotics and related diciplines.
  
  - [Offical Dataset of ShapeNet](https://shapenet.org/)
  - [author's training dataset](https://drive.google.com/drive/folders/1SRJdYDkVDU9Li5oNFVPOutJzbrW7KQ-b?usp=share_link)
  - [pre-trained models](https://drive.google.com/drive/folders/1sH7v2xmQ6ImC4rll28mktEK4hucFO_yz?usp=share_link)
  
  
  ### How to use
  
  Train and test snippets for both auto-encoder and generator are published under the official GitHub repository above.
  
  ### BibTeX Entry and Citation Info
   ```
  @inproceedings{luo2021diffusion,
    author = {Luo, Shitong and Hu, Wei},
    title = {Diffusion Probabilistic Models for 3D Point Cloud Generation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2021}
  }
   ```