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misuka

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Introduction

misuka is a research-focused room acoustic renderer for forward and inverse sound transport simulation, developed in collaboration between the Audio Communication Group and the Computer Graphics Group at TU Berlin.

It is a fully compatible extension to Mitsuba 3, adding plugins for acoustic simulation. The renderer is described in misuka: An Open-Source Differentiable Room Acoustic Renderer.

Main Features

  • Differentiation: misuka is a differentiable renderer, meaning that it can compute derivatives of the entire simulation with respect to input parameters such as material properties, emitter and receiver positions, and scene geometry. It implements Time-Resolved Path Replay Backpropagation for efficient gradient estimation.

  • Cross-platform: misuka has been tested on Linux (x86_64), macOS (arm64), and Windows (x86_64).

  • High performance: The underlying Dr.Jit compiler fuses rendering code into kernels that achieve state-of-the-art performance using an LLVM backend targeting the CPU, a CUDA/OptiX backend targeting NVIDIA GPUs, and a Metal backend targeting Apple Silicon GPUs, all with hardware-accelerated ray tracing.

  • Python first: misuka is deeply integrated with Python. Materials, textures, and even full rendering algorithms can be developed in Python, which the system JIT-compiles (and optionally differentiates) on the fly. This enables the experimentation needed for research.

Disclaimer

misuka is currently under heavy development. Additional features will be added in the near future. The user interface might change.

Tutorials, documentation

You can find tutorials for forward rendering and gradient-based optimization in the folder tutorials_acoustic. More tutorials and a full documentation will be added successively.

Installation

We provide pre-compiled binary wheels via PyPI. Installing misuka this way is as simple as running

pip install misuka

on the command line. The Python package includes the following variants by default:

  • scalar_rgb
  • scalar_acoustic
  • cuda_ad_rgb
  • cuda_ad_acoustic
  • llvm_ad_rgb
  • llvm_ad_acoustic
  • metal_ad_rgb
  • metal_ad_acoustic

Additional variants can be enabled by compiling misuka. Please refer to the Developer's Guide for instructions.

Requirements

  • Python >= 3.9
  • (optional) For computation on the GPU: Nvidia driver >= 535
  • (optional) For vectorized / parallel computation on the CPU: LLVM >= 15
  • (optional) For computation on Apple Silicon GPUs: macOS 15 or newer with a Metal-capable GPU

The optional ones are loaded from your system at runtime rather than shipped with misuka. See Runtime requirements for how to install them and, where needed, how to point misuka at them.

License

misuka is licensed under the PolyForm Noncommercial License 1.0.0, which permits academic and private use. Files inherited from Mitsuba 3 remain under the original BSD-3-Clause license. See LICENSE for the file-level rules.

If you are interested in using misuka commercially, please contact a.jueterbock@tu-berlin.de.

Citation

When using misuka in academic projects, please cite:

@article{misuka,
  title = {{{misuka}}: {{An}} Open-Source Differentiable Room Acoustic Renderer},
  shorttitle = {Misuka},
  author = {J{\"u}terbock, Tobias and Finnendahl, Ugo and Worchel, Markus and Wujecki, Daniel and Alexa, Marc and Weinzierl, Stefan},
  year = 2025,
  journal = {Proceedings of Meetings on Acoustics},
  volume = {58},
  number = {1},
  pages = {022004:1--022004:13},
  publisher = {Acoustical Society of America},
  doi = {10.1121/2.0002193},
  issn = {1939-800X},
}

If your work uses Time-Resolved Path Replay Backpropagation, please also cite:

@article{acoustic_prb,
  title = {Differentiable Geometric Acoustic Path Tracing Using Time-Resolved Path Replay Backpropagation},
  author = {Finnendahl, Ugo and Worchel, Markus and J{\"u}terbock, Tobias and Wujecki, Daniel and Brinkmann, Fabian and Weinzierl, Stefan and Alexa, Marc},
  year = 2025,
  month = jul,
  issue_date = {August 2025},
  journal = {ACM Transactions on Graphics},
  volume = {44},
  number = {4},
  pages = {82:1--82:17},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  issn = {0730-0301},
  doi = {10.1145/3730900},
}

Project links

Data verified by PyPI on Aug 28, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for tjueterb from gravatar.com tjueterb

Credits

Author: Tobias Jüterbock, Ugo Finnendahl, Markus Worchel

GitHub Statistics

Data verified by PyPI on Aug 28, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

PolyForm-Noncommercial-1.0.0 AND BSD-3-Clause
View SPDX License List

Requires

Python >=3.9

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