Mitsuba 3

Mitsuba 3 is a research-oriented rendering system developed at EPFL by Wenzel Jakob and collaborators. Its defining feature is a retargetable architecture built on Dr.Jit, a just-in-time compiler for differentiable computation. This means the same rendering code can be compiled for different execution backends — scalar (debugging), LLVM (optimized CPU), and CUDA (GPU) — and can optionally track derivatives through the entire rendering process for inverse rendering and gradient-based optimization.

As a physically based renderer, Mitsuba 3 supports unbiased path tracing, bidirectional methods, spectral rendering across arbitrary wavelength ranges, and polarization-aware light transport. Its differentiable rendering capability enables applications in inverse rendering, material estimation, neural scene optimization, and gradient-based shape reconstruction — areas at the forefront of computer graphics and vision research.

Mitsuba 3 provides comprehensive Python bindings through which nearly all functionality is accessible, making it particularly popular in the machine learning and differentiable rendering research communities. The system includes a rich library of BSDFs, emitters, sensors, and integrators, and can read scene descriptions in its own XML-based format. While it lacks a built-in GUI, its Python API enables tight integration with scientific workflows, Jupyter notebooks, and deep learning frameworks like PyTorch and JAX.