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## Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering
**Modular Primitives for High-Performance Differentiable Rendering**<br>
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila<br>
[http://arxiv.org/abs/2011.03277](http://arxiv.org/abs/2011.03277)
Nvdiffrast is a PyTorch/TensorFlow library that provides high-performance primitive operations for rasterization-based differentiable rendering.
Please refer to ☞☞ [nvdiffrast documentation](https://nvlabs.github.io/nvdiffrast) ☜☜ for more information.
## Licenses
Copyright © 2020, NVIDIA Corporation. All rights reserved.
This work is made available under the [Nvidia Source Code License](https://github.com/NVlabs/nvdiffrast/blob/main/LICENSE.txt).
For business inquiries, please contact [researchinquiries@nvidia.com](mailto:researchinquiries@nvidia.com)
We do not currently accept outside code contributions in the form of pull requests.
Environment map stored as part of `samples/data/envphong.npz` is derived from a Wave Engine
[sample material](https://github.com/WaveEngine/Samples/tree/master/Materials/EnvironmentMap/Content/Assets/CubeMap.cubemap)
originally shared under
[MIT License](https://github.com/WaveEngine/Samples/blob/master/LICENSE.md).
Mesh and texture stored as part of `samples/data/earth.npz` are derived from
[3D Earth Photorealistic 2K](https://www.turbosquid.com/3d-models/3d-realistic-earth-photorealistic-2k-1279125)
model originally made available under
[TurboSquid 3D Model License](https://blog.turbosquid.com/turbosquid-3d-model-license/#3d-model-license).
## Citation
```
@article{Laine2020diffrast,
title = {Modular Primitives for High-Performance Differentiable Rendering},
author = {Samuli Laine and Janne Hellsten and Tero Karras and Yeongho Seol and Jaakko Lehtinen and Timo Aila},
journal = {ACM Transactions on Graphics},
year = {2020},
volume = {39},
number = {6}
}
```
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