A Genetic Algorithm Based Heterogeneous Subsurface Scattering Representation

Abstract

In this paper, we present a novel heterogeneous subsurface scattering (sss) representation, which is based on a combination of Singular Value Decomposition (SVD) and genetic optimization techniques. To find the best transformation that is applied to measured subsurface scattering data, we use a genetic optimization framework, which tries various transformations to the measured heterogeneous subsurface scattering data to find the fittest one. After we apply the best transformation, we compactly represent measured subsurface scattering data by separately applying the SVD per-color channel of the transformed profiles. In order to get a compact and accurate representation, we apply the SVD on the model errors, iteratively. We validate our approach on a range of optically thick, real-world translucent materials. It's shown that our genetic algorithm based heterogeneous subsurface scattering representation achieves greater visual accuracy than alternative techniques for the same level of compression.