{"schema":"record/v1","id":"src_01M6VBBK4TY4GSEH3SA0SSFEAC","type":"source","revision":1,"etag":"\"r1.s0\"","title":"Mitsuba 3: retargetable, differentiable research renderer","summary":"Mitsuba 3 is a research-oriented rendering system for forward and inverse light transport simulation developed at EPFL. It has a core library and plugins for materials, light sources and rendering algorithms, and is retargetable through the Dr.Jit just-in-time compiler: the same code can run scalar RGB transport or differentiable spectral transport on the GPU. It is Python-first, differentiable, and supports monochromatic, RGB and spectral modes with optional polarization.","status":"active","body":{"text":"What it supplies: Mitsuba 3 is a research-oriented rendering system for forward and inverse light transport simulation developed at EPFL. It has a core library and plugins for materials, light sources and rendering algorithms, and is retargetable through the Dr.Jit just-in-time compiler: the same code can run scalar RGB transport or differentiable spectral transport on the GPU. It is Python-first, differentiable, and supports monochromatic, RGB and spectral modes with optional polarization.\n\nUseful for: - Render physically based images, including spectral and polarised light, from Python.\n- Optimise scene parameters (camera pose, geometry, BSDFs, textures, volumes) by differentiable rendering.\n- Write EXR output for use in compositing or analysis.\n\nFirst action: - Run pip install mitsuba (Python 3.10 or later).\n- Set a variant such as scalar_rgb, load a scene (the README example loads the built-in Cornell box) and call mi.render.\n- Write the image with mi.Bitmap(img).write('cbox.exr') and follow the tutorial videos and notebooks.\n\nPrerequisites and limits: Python 3.10 or newer; optional GPU computation needs an Nvidia driver 535 or later, and optional vectorised CPU computation needs LLVM 18 or later. The PyPI wheels include thirteen variants (scalar, LLVM and CUDA with RGB, mono and spectral options); other variants need a custom Dr.Jit build with CMake. Tested on Linux x86_64, macOS (aarch64 and x86_64) and Windows x86_64.\n\n- It is a research system, aimed at experimentation more than production rendering pipelines.\n- Extra variants require compiling Dr.Jit.\n- GPU use needs NVIDIA hardware with a recent driver.\n\nThis README does not state a licence in the text read here, so check the repository's licence file before use. Images you render carry the rights of the scene assets used.\n\nWritten from the README text only. 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The PyPI wheels include thirteen variants (scalar, LLVM and CUDA with RGB, mono and spectral options); other variants need a custom Dr.Jit build with CMake. Tested on Linux x86_64, macOS (aarch64 and x86_64) and Windows x86_64.\n\n- It is a research system, aimed at experimentation more than production rendering pipelines.\n- Extra variants require compiling Dr.Jit.\n- GPU use needs NVIDIA hardware with a recent driver.\n\nThis README does not state a licence in the text read here, so check the repository's licence file before use. Images you render carry the rights of the scene assets used.\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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It has a core library and plugins for materials, light sources and rendering algorithms, and is retargetable through the Dr.Jit just-in-time compiler: the same code can run scalar RGB transport or differentiable spectral transport on the GPU. It is Python-first, differentiable, and supports monochromatic, RGB and spectral modes with optional polarization.\n\nUseful for: - Render physically based images, including spectral and polarised light, from Python.\n- Optimise scene parameters (camera pose, geometry, BSDFs, textures, volumes) by differentiable rendering.\n- Write EXR output for use in compositing or analysis.\n\nFirst action: - Run pip install mitsuba (Python 3.10 or later).\n- Set a variant such as scalar_rgb, load a scene (the README example loads the built-in Cornell box) and call mi.render.\n- Write the image with mi.Bitmap(img).write('cbox.exr') and follow the tutorial videos and notebooks.\n\nPrerequisites and limits: Python 3.10 or newer; optional GPU computation needs an Nvidia driver 535 or later, and optional vectorised CPU computation needs LLVM 18 or later. The PyPI wheels include thirteen variants (scalar, LLVM and CUDA with RGB, mono and spectral options); other variants need a custom Dr.Jit build with CMake. Tested on Linux x86_64, macOS (aarch64 and x86_64) and Windows x86_64.\n\n- It is a research system, aimed at experimentation more than production rendering pipelines.\n- Extra variants require compiling Dr.Jit.\n- GPU use needs NVIDIA hardware with a recent driver.\n\nThis README does not state a licence in the text read here, so check the repository's licence file before use. Images you render carry the rights of the scene assets used.\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/mitsuba-renderer/mitsuba3/aa01500f57770a4ef0b067693cae08b3e8a2ac73/README.md ; 6603 bytes ; sha256 7bafe93bbb88ef13ca2ef72e259eb6d736d5b5491b4470559c463d9569c6485b ; whole README text","revision":{"ref":"commit aa01500f57770a4ef0b067693cae08b3e8a2ac73","pinned":false,"source_url":"https://raw.githubusercontent.com/mitsuba-renderer/mitsuba3/aa01500f57770a4ef0b067693cae08b3e8a2ac73/README.md"},"checked_at":"2026-11-07T11:23:05.139Z","usefulness":"- Render physically based images, including spectral and polarised light, from Python.\n- Optimise scene parameters (camera pose, geometry, BSDFs, textures, volumes) by differentiable rendering.\n- Write EXR output for use in compositing or analysis.","source_type":"repository","availability":"available","source_owner":"Mitsuba developers at EPFL (mitsuba3) (declared repository namespace)","canonical_url":"https://github.com/mitsuba-renderer/mitsuba3","known_limitations":["Python 3.10 or newer; optional GPU computation needs an Nvidia driver 535 or later, and optional vectorised CPU computation needs LLVM 18 or later. The PyPI wheels include thirteen variants (scalar, LLVM and CUDA with RGB, mono and spectral options); other variants need a custom Dr.Jit build with CMake. Tested on Linux x86_64, macOS (aarch64 and x86_64) and Windows x86_64.\n\n- It is a research system, aimed at experimentation more than production rendering pipelines.\n- Extra variants require compiling Dr.Jit.\n- GPU use needs NVIDIA hardware with a recent driver.\n\nThis README does not state a licence in the text read here, so check the repository's licence file before use. Images you render carry the rights of the scene assets used.\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":"Mitsuba 3: retargetable, differentiable research renderer","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":"Mitsuba 3 is a research-oriented rendering system for forward and inverse light transport simulation developed at EPFL. It has a core library and plugins for materials, light sources and rendering algorithms, and is retargetable through the Dr.Jit just-in-time compiler: the same code can run scalar RGB transport or differentiable spectral transport on the GPU. 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