Cost-Driven Multiple Importance Sampling for Monte-Carlo Rendering

Ferenc Csonka, László Antal Görgy
Department of Control Engineering and Information Technology, Technical University of Budapest,
Budapest, Pázmány P. rkp. 1/D, HUNGARY


The global illumination or transport problems can also be considered as a sequence of integrals, while its Monte-Carlo solutions as different sampling techniques. Multiple importance sampling takes advantage of different sampling strategies and combines the results obtained with them. In this paper we propose the combination of very different global illumination algorithms in a way that their strengths can be preserved. To do this, we generalize the fundamental theory of multiple importance sampling for sequences of integrals and also take into account the computational cost associated with individual sampling techniques. The theoretical results are used to combine bi-directional path tracing and ray-bundles based stochastic iteration.


Multiple importance sampling, stochastic iteration, random walk.