Modular Primitives for High-Performance Differentiable Rendering
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila
http://arxiv.org/abs/2011.03277
Nvdiffrast is a PyTorch library that provides high-performance primitive operations for rasterization-based differentiable rendering.
To install:
pip install setuptools wheel ninja
pip install git+https://github.com/NVlabs/nvdiffrast.git --no-build-isolation
See ☞☞ nvdiffrast documentation ☜☜ for more information.
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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model originally made available under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.
@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}
}