README.md

---
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

```python
# Train an auto-encoder
python train_ae.py 

# Train a generator
python train_gen.py
```

```python
# Test an auto-encoder
python test_ae.py --ckpt ./pretrained/AE_all.pt --categories all

# Test a generator
python test_gen.py --ckpt ./pretrained/GEN_airplane.pt --categories airplane
```

### 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}
}
 ```