{"schema":"record/v1","id":"src_01M6VBBDRJX2SKSQF9JS43WFNC","type":"source","revision":1,"etag":"\"r1.s0\"","title":"Kaolin: NVIDIA PyTorch library for 3D deep learning, Gaussian splats and differentiable rendering","summary":"Kaolin is NVIDIA's PyTorch library of reusable, GPU-optimised modules distilled from 3D deep learning research. The README lists representation-agnostic physics for meshes, splats and point clouds (Simplicits), differentiable rendering (DIB-R, nvdiffrast, an easy_render PBR path), first-class 3D Gaussian splat support (PLY and USD I/O, densification, gsplat bridge), GPU octree structured point clouds, conversions between 3D representations, quaternion operations and USD I/O.","status":"active","body":{"text":"What it supplies: Kaolin is NVIDIA's PyTorch library of reusable, GPU-optimised modules distilled from 3D deep learning research. The README lists representation-agnostic physics for meshes, splats and point clouds (Simplicits), differentiable rendering (DIB-R, nvdiffrast, an easy_render PBR path), first-class 3D Gaussian splat support (PLY and USD I/O, densification, gsplat bridge), GPU octree structured point clouds, conversions between 3D representations, quaternion operations and USD I/O.\n\nUseful for: - Load, optimise and render 3D Gaussian splat scenes.\n- Simulate objects with collisions from a splat or mesh.\n- Read and write USD-based 3D data in research pipelines.\n- Prototype interactive browser interfaces over 3D research using the client-server visualisation framework.\n\nFirst action: - Pick your PyTorch and CUDA versions, then install a pre-built wheel such as pip install kaolin==0.18.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.8.0_cu128.html.\n- Check the installation guide's compatibility matrix.\n- Try the tutorials linked for Gaussian splats and simulation.\n\nPrerequisites and limits: Pre-built wheels exist per torch and CUDA combination (the README's example uses kaolin 0.18.0 with PyTorch 2.8.0 and CUDA 12.8); other combinations need the compatibility matrix or a source install. It gives no VRAM figures in the text read.\n\n- The kaolin/non_commercial package is legacy and kept only for backward compatibility; new code should use the Apache-licensed modules.\n- Wheel availability is tied to specific torch and CUDA versions.\n- It is a research library.\n\nThe README states Kaolin is released under the Apache License 2.0, with a legacy kaolin/non_commercial package kept for old import paths that new code should avoid. Data you process carries its own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. \n\nSource inspected: https://raw.githubusercontent.com/NVIDIAGameWorks/kaolin/ee8b7eca1e56b1b9fd1509fc406f8d8ccbc9240b/README.md ; 16458 bytes ; sha256 45eb06863d7269c1a8e7ec585bacf57dfde448299782c9392dbead64c591266d ; whole README text","archived":null,"revision":{"ref":"commit ee8b7eca1e56b1b9fd1509fc406f8d8ccbc9240b","pinned":false,"source_url":"https://raw.githubusercontent.com/NVIDIAGameWorks/kaolin/ee8b7eca1e56b1b9fd1509fc406f8d8ccbc9240b/README.md"},"checked_at":"2026-11-07T11:23:04.019Z","parent_url":null,"usefulness":"- Load, optimise and render 3D Gaussian splat scenes.\n- Simulate objects with collisions from a splat or mesh.\n- Read and write USD-based 3D data in research pipelines.\n- Prototype interactive browser interfaces over 3D research using the client-server visualisation framework.","source_type":"repository","availability":"available","dependencies":[],"source_owner":"NVIDIA (Kaolin) (declared repository namespace)","canonical_url":"https://github.com/nvidiagameworks/kaolin","next_check_at":null,"successor_url":null,"hardware_notes":null,"known_limitations":["Pre-built wheels exist per torch and CUDA combination (the README's example uses kaolin 0.18.0 with PyTorch 2.8.0 and CUDA 12.8); other combinations need the compatibility matrix or a source install. It gives no VRAM figures in the text read.\n\n- The kaolin/non_commercial package is legacy and kept only for backward compatibility; new code should use the Apache-licensed modules.\n- Wheel availability is tied to specific torch and CUDA versions.\n- It is a research library.\n\nThe README states Kaolin is released under the Apache License 2.0, with a legacy kaolin/non_commercial package kept for old import paths that new code should avoid. Data you process carries its own rights.\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.","Source-documented steps; execution not performed. 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The README lists representation-agnostic physics for meshes, splats and point clouds (Simplicits), differentiable rendering (DIB-R, nvdiffrast, an easy_render PBR path), first-class 3D Gaussian splat support (PLY and USD I/O, densification, gsplat bridge), GPU octree structured point clouds, conversions between 3D representations, quaternion operations and USD I/O.\n\nUseful for: - Load, optimise and render 3D Gaussian splat scenes.\n- Simulate objects with collisions from a splat or mesh.\n- Read and write USD-based 3D data in research pipelines.\n- Prototype interactive browser interfaces over 3D research using the client-server visualisation framework.\n\nFirst action: - Pick your PyTorch and CUDA versions, then install a pre-built wheel such as pip install kaolin==0.18.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.8.0_cu128.html.\n- Check the installation guide's compatibility matrix.\n- Try the tutorials linked for Gaussian splats and simulation.\n\nPrerequisites and limits: Pre-built wheels exist per torch and CUDA combination (the README's example uses kaolin 0.18.0 with PyTorch 2.8.0 and CUDA 12.8); other combinations need the compatibility matrix or a source install. It gives no VRAM figures in the text read.\n\n- The kaolin/non_commercial package is legacy and kept only for backward compatibility; new code should use the Apache-licensed modules.\n- Wheel availability is tied to specific torch and CUDA versions.\n- It is a research library.\n\nThe README states Kaolin is released under the Apache License 2.0, with a legacy kaolin/non_commercial package kept for old import paths that new code should avoid. Data you process carries its own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. \n\nSource inspected: https://raw.githubusercontent.com/NVIDIAGameWorks/kaolin/ee8b7eca1e56b1b9fd1509fc406f8d8ccbc9240b/README.md ; 16458 bytes ; sha256 45eb06863d7269c1a8e7ec585bacf57dfde448299782c9392dbead64c591266d ; whole README text","revision":{"ref":"commit ee8b7eca1e56b1b9fd1509fc406f8d8ccbc9240b","pinned":false,"source_url":"https://raw.githubusercontent.com/NVIDIAGameWorks/kaolin/ee8b7eca1e56b1b9fd1509fc406f8d8ccbc9240b/README.md"},"checked_at":"2026-11-07T11:23:04.019Z","usefulness":"- Load, optimise and render 3D Gaussian splat scenes.\n- Simulate objects with collisions from a splat or mesh.\n- Read and write USD-based 3D data in research pipelines.\n- Prototype interactive browser interfaces over 3D research using the client-server visualisation framework.","source_type":"repository","availability":"available","source_owner":"NVIDIA (Kaolin) (declared repository namespace)","canonical_url":"https://github.com/nvidiagameworks/kaolin","known_limitations":["Pre-built wheels exist per torch and CUDA combination (the README's example uses kaolin 0.18.0 with PyTorch 2.8.0 and CUDA 12.8); other combinations need the compatibility matrix or a source install. It gives no VRAM figures in the text read.\n\n- The kaolin/non_commercial package is legacy and kept only for backward compatibility; new code should use the Apache-licensed modules.\n- Wheel availability is tied to specific torch and CUDA versions.\n- It is a research library.\n\nThe README states Kaolin is released under the Apache License 2.0, with a legacy kaolin/non_commercial package kept for old import paths that new code should avoid. Data you process carries its own rights.\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.","Source-documented steps; execution not performed. Internal affiliated review is not independent validation."]},"tags":[],"type":"source","title":"Kaolin: NVIDIA PyTorch library for 3D deep learning, Gaussian splats and differentiable rendering","rights":[{"notes":"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.","hosting":"permitted","component":"documentation","license_id":null,"modification":"permitted","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Kaolin is NVIDIA's PyTorch library of reusable, GPU-optimised modules distilled from 3D deep learning research. 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