Martin Sik

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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(More)
Efficiently computing light transport in participating media in a manner that is robust to variations in media density, scattering albedo, and anisotropy is a difficult and important problem in realistic image synthesis. While many specialized rendering techniques can efficiently resolve subsets of transport in specific media, no single approach can(More)
Markov Chain Monte Carlo (MCMC) has recently received a lot of attention in light transport simulation research [Hanika et al. 2015; Hachisuka et al. 2014]. While these methods aim at high quality sampling of local extremes of the path space (so called <i>local exploration</i>), the other issue - discovering these extremes - has been so far neglected. Poor(More)
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