GenSSS: a genetic algorithm for measured subsurface scattering representation

Abstract

We present a novel genetic algorithm-based approach for the compact representation of heterogeneous, optically thick, translucent materials. Utilizing genetic optimization, we also find the best transformation to represent measured subsurface scattering data. We employ a factored subsurface scattering representation, based on a singular value decomposition (SVD), separately applying the SVD per-color channel of the transformed profiles. In order to achieve a compact, accurate representation, we perform this iteratively on the model errors. By allowing the number of iterations to be customized, our representation provides a mechanism to trade the visual quality possible against the level of compression achieved through our representation. We validate our approach by analyzing a range of real-world translucent materials, geometries and lighting conditions. For heterogeneous translucent materials, we further demonstrate that for the same level of compression, our method achieves greater visual accuracy than alternative techniques. Finally, we present an application of our factored representation, which can be used to convert heterogeneous materials into homogeneous material representations.