On-line Learning of Parametric Mixture Models for Light Transport Simulation
|Plain Bidirectional path tracing (BDPT)
||Our guided BDPT
Comparison of the result of plain bidirectional pah tracing (BDPT) and BDPT
guided by our parametric distributions trained in 30 training passes in one-hour rendering
of a scene featuring difficult visibility.
The results show that the time spent on distributin training is quickly amortized by the
superior performance of the subsequent guided rendering.
Monte Carlo techniques for light transport simulation rely on importance sampling when constructing light transport paths. Previous work has shown that suitable sampling distributions can be recovered from particles distributed in the scene prior to rendering. We propose to represent the distributions by a parametric mixture model trained in an on-line (i.e. progressive) manner from a potentially infinite stream of particles. This enables recovering good sampling distributions in scenes with complex lighting, where the necessary number of particles may exceed available memory. Using these distributions for sampling scattering directions and light emission significantly improves the performance of state-of-the-art light transport simulation algorithms when dealing with complex lighting.
Jiří Vorba, Ondřej Karlík, Martin Šik, Tobias Ritschel, and Jaroslav Křivánek.
On-line Learning of Parametric Mixture Models for Light Transport Simulation.
ACM Trans. Graph., SIGGRAPH 2014.
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The work was supported by Charles University in Prague
, project GA UK No 580612,
by the grant SVV–2014–260103, and by the Czech Science Foundation
We thank Jan Beneš, Alexander Wilkie, and Iliyan Georgiev for their feedback on the paper
draft and to Ludvík Koutný for providing us with the Living Room scene.
We have used these two renderers: