A Neuro-Evolutionary Unbiased Global Illumination Algorithm

In this paper we present a two pass unbiased global illumina­tion rendering algorithm. First pass calculations are done shooting rays from light sources and storing directional information in a growing adap­tive neural gas structure. The second pass is a ray tracing process which uses this information to create an evolving population of rays that tend to optimally sample their surroundings. Finally, weighted Monte Carlo integration is used with a dynamic Voronoi diagram to reduce uncer­tainty in the solution.

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