Efficient Schemes for Monte Carlo Markov Chain Algorithms in Global Illumination
Current MCMC algorithms are limited from achieving high rendering efficiency due to possibly high failure rates in caustics pertur- bations and stratified exploration of the image plane. In this paper we improve the MCMC approach significantly by introducing new lens per- turbation and new path-generation methods. The new lens perturbation method simplifies the computation and control of caustics perturbation and can increase the perturbation success rate. The new path-generation methods aim to concentrate more computation on âhigh perceptual vari- anceâ regions and âhard-to-find-but-importantâ paths. We implement these schemes in the Population Monte Carlo Energy Redistribution framework to demonstrate the effectiveness of these improvements. In addition., we discuss how to add these new schemes into the Energy Re- distribution Path Tracing and Metropolis Light Transport algorithms. Our results show that rendering efficiency is improved with these new schemes.
Yu-Chi Lai, Feng Liu, Li Zhang, Charles Dyer Efficient Schemes for Monte Carlo Markov Chain Algorithms in Global Illumination, Proc. 4th International Symposium on Visual Computing, 2008
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