## Nvdiffrast &ndash; 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 &#x261E;&#x261E; [nvdiffrast documentation](https://nvlabs.github.io/nvdiffrast) &#x261C;&#x261C; for more information. ## Licenses Copyright &copy; 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 visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/) 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-2.5/tree/master/Materials/EnvironmentMap/Content/Assets/CubeMap.cubemap) originally shared under [MIT License](https://github.com/WaveEngine/Samples-2.5/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[3D Earth Photorealistic 2K](https://www.turbosquid.com/3d-models/3d-realistic-earth-photorealistic-2k-1279125) model originally made available under [TurboSquid 3D Model License](https://blog.turbosquid.com/turbosquid-3d-model-license/#3d-model-license).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} } ```