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Geometric Sample Reweighting for Monte Carlo Integration

Jerry Guo and Elmar Eisemann

Four highly glossy spheres moving in different directions with 64 samples per pixel. In each subfigure: corresponding render, difference with reference and highlighted regions.

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We presentageneral sample reweighting scheme and its underlying theory for the integration of an unknown function with low dimensionality. Our method produces better results than standard weighting schemes for com-mon sampling strategies, while avoiding bias. Our main insight is to link the weight derivation to the function reconstruction process during integration. The implementation of our solution is simple and results in an improved multiple Monte Carlo rendering problems.

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Citation

Jerry Guo and Elmar Eisemann, Geometric Sample Reweighting for Monte Carlo Integration, CoRR, abs/1908.01809, 2019.

BibTex 

@article{bib:guo:2019,
    author       = { Guo, Jerry and Eisemann, Elmar },    
    title        = { Geometric Sample Reweighting for Monte Carlo Integration },
    journal      = { CoRR },
    volume       = { abs/1908.01809 },
    year         = { 2019 },
    dblp         = { journals/corr/abs-1908-01809 },
    url          = { https://publications.graphics.tudelft.nl/papers/109 },
}