“Continuous multiple importance sampling” by West, Georgiev, Gruson and Hachisuka

  • ©Rex West, Iliyan Georgiev, Adrien Gruson, and Toshiya Hachisuka



Session Title:

    Monte Carlo and Perception


    Continuous multiple importance sampling



    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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