View Full Version : Mitsuba Renderer v0.4.0 released

10-03-2012, 11:04 AM
i never saw this great renderer mentioned on this forum
so i thought i give you a heads up



10-03-2012, 11:35 AM
Have you tested this already with LW DAE files?

10-05-2012, 10:31 AM
I tried it a bit, it seems still immature program! Lw DAE, somehow "twist" scene upside down and still can not figure out how to import lights from Lw... kinda looks like it's still in beta!?

03-03-2013, 11:14 PM
New update of multiengine physically based renderer Mitsuba 0.4.4
May be someone day we got something like Mitsuba Renderer in additional to LW Render Engine?

Manifold Exploration talk
This 17 minute talk explains the mathematics behind the paper "Manifold Exploration: A Markov Chain Monte Carlo technique for rendering scenes with difficult specular transport" by Wenzel Jakob and Steve Marschner.

More information is available here: cs.cornell.edu/projects/manifolds-sg12


It is a long-standing problem in unbiased Monte Carlo methods for rendering that certain difficult types of light transport paths, particularly those involving viewing and illumination along paths containing specular or glossy surfaces, cause unusably slow convergence. In this paper we introduce Manifold Exploration, a new way of handling specular paths in rendering. It is based on the idea that sets of paths contributing to the image naturally form manifolds in path space, which can be explored locally by a simple equation-solving iteration. This paper shows how to formulate and solve the required equations using only geometric information that is already generally available in ray tracing systems, and how to use this method in in two different Markov Chain Monte Carlo frameworks to accurately compute illumination from general families of paths. The resulting rendering algorithms handle specular, near-specular, glossy, and diffuse surface interactions as well as isotropic or highly anisotropic volume scattering interactions, all using the same fundamental algorithm. An implementation is demonstrated on a range of challenging scenes and evaluated against previous methods.