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Metadata-Version: 2.1 |
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Name: nvdiffrast |
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Version: 0.2.5 |
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Summary: nvdiffrast - modular primitives for high-performance differentiable rendering |
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Home-page: https: |
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Author: Samuli Laine |
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Author-email: slaine@nvidia.com |
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License: UNKNOWN |
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Platform: UNKNOWN |
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Classifier: Programming Language :: Python :: 3 |
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Classifier: Operating System :: OS Independent |
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Requires-Python: >=3.6 |
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Description-Content-Type: text/markdown |
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License-File: LICENSE.txt |
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## Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering |
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![Teaser image](./docs/img/teaser.png) |
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**Modular Primitives for High-Performance Differentiable Rendering**<br> |
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Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila<br> |
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[http: |
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Nvdiffrast is a PyTorch/TensorFlow library that provides high-performance primitive operations for rasterization-based differentiable rendering. |
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Please refer to ☞☞ [nvdiffrast documentation](https: |
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## Licenses |
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Copyright © 2020, NVIDIA Corporation. All rights reserved. |
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This work is made available under the [Nvidia Source Code License](https: |
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For business inquiries, please contact [researchinquiries@nvidia.com](mailto:researchinquiries@nvidia.com) |
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We do not currently accept outside code contributions in the form of pull requests. |
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Environment map stored as part of `samples/data/envphong.npz` is derived from a Wave Engine |
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[sample material](https: |
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originally shared under |
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[MIT License](https: |
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Mesh and texture stored as part of `samples/data/earth.npz` are derived from |
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[3D Earth Photorealistic 2K](https: |
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model originally made available under |
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[TurboSquid 3D Model License](https: |
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## Citation |
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``` |
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@article{Laine2020diffrast, |
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title = {Modular Primitives for High-Performance Differentiable Rendering}, |
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author = {Samuli Laine and Janne Hellsten and Tero Karras and Yeongho Seol and Jaakko Lehtinen and Timo Aila}, |
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journal = {ACM Transactions on Graphics}, |
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year = {2020}, |
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volume = {39}, |
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number = {6} |
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} |
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``` |
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