Nvdiffrast Save

Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering

Project README

Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering

Teaser image

Modular Primitives for High-Performance Differentiable Rendering
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila

Nvdiffrast is a PyTorch/TensorFlow library that provides high-performance primitive operations for rasterization-based differentiable rendering. Please refer to ☞☞ nvdiffrast documentation ☜☜ for more information.


Copyright © 2020–2022, NVIDIA Corporation. All rights reserved.

This work is made available under the Nvidia Source Code License.

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing

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 originally shared under MIT License. Mesh and texture stored as part of samples/data/earth.npz are derived from 3D Earth Photorealistic 2K model originally made available under TurboSquid 3D Model License.


  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}
Open Source Agenda is not affiliated with "Nvdiffrast" Project. README Source: NVlabs/nvdiffrast
Open Issues
Last Commit
1 month ago

Open Source Agenda Badge

Open Source Agenda Rating