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Parametric Bidirectional Scattering Distribution Functions (BSDFs) arepervasively used because of their flexibility to represent a large variety ofmaterial appearances by simply tuning the parameters. While efficientevaluation of parametric BSDFs has been well-studied, high-quality importancesampling techniques for parametric BSDFs are still scarce. Existing samplingstrategies either heavily rely on approximations, resulting in high variance,or solely perform sampling on a portion of the whole BSDF slice. Moreover, manyof the sampling approaches are specifically paired with certain types of BSDFs.In this paper, we seek an efficient and general way for importance samplingparametric BSDFs. We notice that the nature of importance sampling is themapping between a uniform distribution and the target distribution.Specifically, when BSDF parameters are given, the mapping that performsimportance sampling on a BSDF slice can be simply recorded as a 2D image thatwe name as importance map. Following this observation, we accurately precomputethe importance maps using a mathematical tool named optimal transport. Then wepropose a lightweight neural network to efficiently compress the precomputedimportance maps. In this way, we have brought parametric BSDF importantsampling to the precomputation stage, avoiding heavy runtime computation. Sincethis process is similar to light baking where a set of images are precomputed,we name our method importance baking. Together with a BSDF evaluation networkand a PDF (probability density function) query network, our method enables fullmultiple importance sampling (MIS) without any revision to the renderingpipeline. Our method essentially performs perfect importance sampling. Comparedwith previous methods, we demonstrate reduced noise levels on rendering resultswith a rich set of appearances.

BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling General Parametric BSDFs