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Towards a Principled Kernel Prediction for Spatially Varying BSSRDFs

Publication at Faculty of Mathematics and Physics |
2018

Abstract

While the modeling of sub-surface translucency using homogeneous BSSRDFs is an established industry standard, applying the same approach to heterogeneous materials is predominantly heuristical. 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 forplanarmaterialswithspatiallyvaryingscatteringalbedo,withaddeddiscussionaboutextendingtheapproach for general geometries and full heterogeneity of the material parameters.