| --- |
| license: other |
|
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| tags: |
| - diffusion |
| - point-cloud |
| - airplane |
| - 3D |
|
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| 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. |
|
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|
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| ### 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) |
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|
|
| ### How to use |
|
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| 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} |
| } |
| ``` |
