Fusion of Monte Carlo and Quasi-Monte Carlo for Global Illumination

Monte Carlo method performs well in the computation of complex global illumination problem because it is general,robust and independent of dimension.However,the synthetic image produced by Monte Carlo is too noisy.Low dimensional quas-i Monte Carlo integration for continuous integrand converges faster than Monte Carlo,but quas-i Monte Carlo is not fit for complex global illumination when it is used directly.In this paper,we study the sampling procedure for the random walk,which is the fundamental of Monte Carlo global illumination.Based on the fusion of Monte Carlo and quas-i Monte Carlo,we present two novel computational strategies for solving the global illumination problem. The two strategies are general and can be used for all of the Monte Carlo based global illumination algorithms.The proposed strategies can not only lead to a reduction of image noises,but it could be also implemented easily without increasing computation and memory costs.