Continuous Multiple Importance Sampling

Rex West, The University of Tokyo, Japan
Iliyan Georgiev, Autodesk, United Kingdom
Adrien Gruson, McGill University, Canada
Toshiya Hachisuka, The University of Tokyo, Japan
In ACM Trans. Graph. (SIGGRAPH), 2020.

Abstract

Multiple importance sampling (MIS) is a provably good way to combine a finite set of sampling techniques to reduce variance in Monte Carlo integral estimation. However, there exist integration problems for which a continuum of sampling techniques is available. To handle such cases we establish a continuous MIS (CMIS) formulation as a generalization of MIS to uncountably infinite sets of techniques. Our formulation is equipped with a base estimator that is coupled with a provably optimal balance heuristic and a practical stochastic MIS (SMIS) estimator that makes CMIS accessible to a broad range of problems. To illustrate the effectiveness and utility of our framework, we apply it to three different light transport applications, showing improved performance over the prior state-of-the-art techniques.

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Cite

@article{West:2020:CMIS,
    author = "West, Rex and Georgiev, Iliyan and Gruson, Adrien and Hachisuka, Toshiya",
    title = "Continuous Multiple Importance Sampling",
    journal= "ACM Transactions on Graphics (TOG)",
    volume = "39",
    number = "4",
    article = "136",
    year = "2020",
    month = jul,
    doi = "10.1145/3386569.3392436"
}
							

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