| | --- |
| | 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} |
| | } |
| | ``` |
