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--- |
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license: other |
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tags: |
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- diffusion |
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- point-cloud |
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- airplane |
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- 3D |
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datasets: |
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- shapenet |
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--- |
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### Model Description |
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– Luo, Shitong and Hu, Wei |
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– 2021 |
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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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### Documents |
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- [GitHub Repo](https://github.com/luost26/diffusion-point-cloud) |
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- [Paper - Diffusion Probabilistic Models for 3D Point Cloud Generation](https://arxiv.org/abs/2103.01458) |
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### Datasets |
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ShapeNet is a comprehensive 3D shape dataset created for research in computer graphics, computer vision, robotics and related diciplines. |
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- [Offical Dataset of ShapeNet](https://shapenet.org/) |
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- [author's training dataset](https://drive.google.com/drive/folders/1SRJdYDkVDU9Li5oNFVPOutJzbrW7KQ-b?usp=share_link) |
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- [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. |
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### BibTeX Entry and Citation Info |
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``` |
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@inproceedings{luo2021diffusion, |
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author = {Luo, Shitong and Hu, Wei}, |
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title = {Diffusion Probabilistic Models for 3D Point Cloud Generation}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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month = {June}, |
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year = {2021} |
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} |
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``` |
