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---
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}
}
``` |