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Product Importance Sampling for Light Transport Path Guiding
Author(s) -
Herholz Sebastian,
Elek Oskar,
Vorba Jiří,
Lensch Hendrik,
Křivánek Jaroslav
Publication year - 2016
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12950
Subject(s) - rendering (computer graphics) , computer science , global illumination , sampling (signal processing) , bidirectional reflectance distribution function , importance sampling , monte carlo method , adaptive sampling , computer vision , path tracing , product (mathematics) , gaussian , mathematical optimization , artificial intelligence , algorithm , reflectivity , mathematics , statistics , optics , physics , geometry , filter (signal processing) , quantum mechanics
The efficiency of Monte Carlo algorithms for light transport simulation is directly related to their ability to importance‐sample the product of the illumination and reflectance in the rendering equation. Since the optimal sampling strategy would require knowledge about the transport solution itself, importance sampling most often follows only one of the known factors – BRDF or an approximation of the incident illumination. To address this issue, we propose to represent the illumination and the reflectance factors by the Gaussian mixture model (GMM), which we fit by using a combination of weighted expectation maximization and non‐linear optimization methods. The GMM representation then allows us to obtain the resulting product distribution for importance sampling on‐the‐fly at each scene point. For its efficient evaluation and sampling we preform an up‐front adaptive decimation of both factor mixtures. In comparison to state‐of‐the‐art sampling methods, we show that our product importance sampling can lead to significantly better convergence in scenes with complex illumination and reflectance.