Mitsuba
Mitsuba is an extensible rendering framework written in
portable C++. It implements unbiased as well as biased techniques and
contains heavy optimizations targeted towards current CPU
architectures.
Mitsuba comes with a command-line interface as well as a
graphical frontend to interactively explore scenes. While navigating, a
rough preview is shown that becomes increasingly accurate as soon as
all movements are stopped. Once a viewpoint has been chosen, a wide
range of rendering techniques can be used to generate images, and their
parameters can be tuned from within the program. .
Mitsuba can transparently distribute work over a cluster
without the need for a shared filesystem. Most implemented algorithms
can be run in parallel over massive numbers of networked cores.
Features include:
- Available rendering techniques:
- Direct illumination
- Monte-Carlo path tracer which solves the full Radiative
Transfer Equation
- Photon mapper with irradiance gradients
- Adjoint particle tracer
- Instant Radiosity (hardware-accelerated)
- Progressive Photon Mapper
- Stochastic Progressive Photon Mapper
- Veach-style Bidirectional Path Tracer
- Kelemen-style Metropolis Light Transport
- Veach-style Metropolis Light Transport
- Supports the most commonly used scattering models:
Lambertian surfaces, ideal dielectrics & mirrors as well as the
the Phong & anisotropic Ward BRDFs
- Compute global illumination solutions in scenes containing
large isotropic or anisotropic participating media
- Internally uses a O(n log n) SAH kd-tree compiler with
support for primitive clipping (aka. perfect splits). The ray tracing
core is built on Havran's fast traversal algorithm
- Data exchange with the major modeling packages is supported
using the COLLADA file format. Mitsuba can read DAE
files and convert them into its native XML-based file format
- Spectral rendering, black body radiation and dispersion
- Customizable image reconstruction filters
- High dynamic-range input/output using the OpenEXR format
- Deterministic Quasi-Monte Carlo sampling
- Adaptive integration
- Depth of field
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Last Updated Sunday, June 10 2012 @ 03:21 AM EDT
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