Towards a Principled Kernel Prediction for Spatially Varying BSSRDFs
Predictions of the spatially varying sub-surface scattering kernels (false-color logarithmic maps) for heterogeneous materials (defined by 2.5D scattering albedo textures shown in the respective thumbnails).
While the modeling of sub-surface translucency using homogeneous BSSRDFs is an established industry standard, applying the same approach to heterogeneous materials is predominantly heuristic. We propose a more principled methodology for obtaining and evaluating a spatially varying BSSRDF, on the basis of the volumetric sub-surface structure of the simulated material. The key ideas enabling this are a simulation-data driven kernel for aggregating the spatially varying material parameters, and a structure-preserving decomposition of the subsurface transport into a local and a global component. Our current results show significantly improved accuracy for planar materials with spatially varying scattering albedo, with added discussion about extending the approach for general geometries and full heterogeneity of the material parameters.
Oskar Elek and Jaroslav KÝivŠnek. Towards a Principled Kernel Prediction for Spatially Varying BSSRDFs. MAM 2018: The Sixth Annual Eurographics Workshop on Material Appearance Modeling, 2018
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We are grateful to Sebastian Herholz and Per Christensen for their feedback, Eugene DíEon for sharing his simulation code, and Wenzel Jakob for developing the open-source renderer Mitsuba used to generate the reference images. This project has received funding from the European Unionís Horizon 2020 research and innovation programme, under the Marie Sklodowska-Curie grant agreement No 642841 (DISTRO), and was further supported by the Czech Science Foundation grant 16-18964S and the Charles University grant SVV-2017-260452.