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nvdiffrast: high-performance differentiable rasterization primitives for PyTorch

Public record src_01M6VBBGH838NK51HWJKGPBB46, revision 1. Source and rights labels remain attached. Linked media has not been viewed by opening this page.

# nvdiffrast: high-performance differentiable rasterization primitives for PyTorch

- id: src_01M6VBBGH838NK51HWJKGPBB46
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T15, T20 · tags: none
- updated: 2026-11-07T15:52:07.432Z
- url: https://veyrul.com/sources/src_01M6VBBGH838NK51HWJKGPBB46

Nvdiffrast is a PyTorch library offering high-performance primitive operations for rasterization-based differentiable rendering, described in a 2020 ACM Transactions on Graphics paper. It is a building block that other 3D reconstruction and generation projects depend on rather than an end-user tool.

Source: https://github.com/nvlabs/nvdiffrast

What it supplies: Nvdiffrast is a PyTorch library offering high-performance primitive operations for rasterization-based differentiable rendering, described in a 2020 ACM Transactions on Graphics paper. It is a building block that other 3D reconstruction and generation projects depend on rather than an end-user tool.

Useful for: - Build optimisation or learning pipelines that need gradients through a rasteriser, such as texture, material or mesh fitting.

First action: - Run pip install setuptools wheel ninja.
- Run pip install git+https://github.com/NVlabs/nvdiffrast.git --no-build-isolation.
- Read the nvdiffrast documentation for the API and samples.

Prerequisites and limits: Installation compiles CUDA code with ninja, so a build toolchain and an NVIDIA setup are implied; the README gives no version or hardware figures in the text read.

- The repository does not accept outside pull requests.
- The README is very short and defers to external documentation.
- Some sample data is derived from third-party material with its own licences.

The README states the work is made available under the Nvidia Source Code License and directs business inquiries to NVIDIA Research Licensing. Sample data is derived from an MIT-licensed Wave Engine material and a TurboSquid 3D Model License earth model.

Written from the README text only. Statements are as the README gives them; nothing was installed or run. 

Source inspected: https://raw.githubusercontent.com/NVlabs/nvdiffrast/253ac4fcea7de5f396371124af597e6cc957bfae/README.md ; 2207 bytes ; sha256 2c53380264d463e2866c04d507404abd9060c5cb9aa02478690f132796537a87 ; whole README text

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  "source_owner": "NVIDIA Research (nvdiffrast) (declared repository namespace)",
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    "Installation compiles CUDA code with ninja, so a build toolchain and an NVIDIA setup are implied; the README gives no version or hardware figures in the text read.\n\n- The repository does not accept outside pull requests.\n- The README is very short and defers to external documentation.\n- Some sample data is derived from third-party material with its own licences.\n\nThe README states the work is made available under the Nvidia Source Code License and directs business inquiries to NVIDIA Research Licensing. Sample data is derived from an MIT-licensed Wave Engine material and a TurboSquid 3D Model License earth model.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. ",
    "Rendered page text of the exact archived capture of the README page, read in full by the author; no media viewed or heard; nothing executed or installed.",
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```

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=permitted training=unknown · Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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