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• W2982565196 abstract "Abstract To increase diversity and realism, surface bidirectional scattering distribution functions (BSDFs) are often modelled as consisting of multiple layers, but accurately evaluating layered BSDFs while accounting for all light transport paths is a challenging problem. Recently, Guo et al . [GHZ18] proposed an accurate and general position‐free Monte Carlo method, but this method introduces variance that leads to longer render time compared to non‐stochastic layered models. We improve the previous work by presenting two new sampling strategies, pair‐product sampling and multiple‐product sampling . Our new methods better take advantage of the layered structure and reduce variance compared to the conventional approach of sequentially sampling one BSDF at a time. Our pair‐product sampling strategy importance samples the product of two BSDFs from a pair of adjacent layers. We further generalize this to multiple‐product sampling , which importance samples the product of a chain of three or more BSDFs. In order to compute these products, we developed a new approximate Gaussian representation of individual layer BSDFs. This representation incorporates spatially varying material properties as parameters so that our techniques can support an arbitrary number of textured layers. Compared to previous Monte Carlo layering approaches, our results demonstrate substantial variance reduction in rendering isotropic layered surfaces." @default.
• W2982565196 created "2019-11-08" @default.
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• W2982565196 date "2019-10-31" @default.
• W2982565196 modified "2023-09-30" @default.
• W2982565196 title "Gaussian Product Sampling for Rendering Layered Materials" @default.
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• W2982565196 doi "https://doi.org/10.1111/cgf.13883" @default.
• W2982565196 hasPublicationYear "2019" @default.
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