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Variance-aware multiple importance sampling

Published: 08 November 2019 Publication History

Abstract

Many existing Monte Carlo methods rely on multiple importance sampling (MIS) to achieve robustness and versatility. Typically, the balance or power heuristics are used, mostly thanks to the seemingly strong guarantees on their variance. We show that these MIS heuristics are oblivious to the effect of certain variance reduction techniques like stratification. This shortcoming is particularly pronounced when unstratified and stratified techniques are combined (e.g., in a bidirectional path tracer). We propose to enhance the balance heuristic by injecting variance estimates of individual techniques, to reduce the variance of the combined estimator in such cases. Our method is simple to implement and introduces little overhead.

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  1. Variance-aware multiple importance sampling

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    cover image ACM Transactions on Graphics
    ACM Transactions on Graphics  Volume 38, Issue 6
    December 2019
    1292 pages
    ISSN:0730-0301
    EISSN:1557-7368
    DOI:10.1145/3355089
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 08 November 2019
    Published in TOG Volume 38, Issue 6

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    Author Tags

    1. bidirectional path tracing
    2. multiple importance sampling
    3. stratification

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