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Selective guided sampling with complete light transport paths

Published: 04 December 2018 Publication History

Abstract

Finding good global importance sampling strategies for Monte Carlo light transport is challenging. While estimators using local methods (such as BSDF sampling or next event estimation) often work well in the majority of a scene, small regions in path space can be sampled insufficiently (e.g. a reflected caustic). We propose a novel data-driven guided sampling method which selectively adapts to such problematic regions and complements the unguided estimator. It is based on complete transport paths, i.e. is able to resolve the correlation due to BSDFs and free flight distances in participating media. It is conceptually simple and places anisotropic truncated Gaussian distributions around guide paths to reconstruct a continuous probability density function (guided PDF). Guide paths are iteratively sampled from the guided as well as the unguided PDF and only recorded if they cause high variance in the current estimator. While plain Monte Carlo samples paths independently and Markov chain-based methods perturb a single current sample, we determine the reconstruction kernels by a set of neighbouring paths. This enables local exploration of the integrand without detailed balance constraints or the need for analytic derivatives. We show that our method can decompose the path space into a region that is well sampled by the unguided estimator and one that is handled by the new guided sampler. In realistic scenarios, we show 4× speedups over the unguided sampler.

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    cover image ACM Transactions on Graphics
    ACM Transactions on Graphics  Volume 37, Issue 6
    December 2018
    1401 pages
    ISSN:0730-0301
    EISSN:1557-7368
    DOI:10.1145/3272127
    Issue’s Table of Contents
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    Publication History

    Published: 04 December 2018
    Published in TOG Volume 37, Issue 6

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    Author Tags

    1. Monte Carlo
    2. global illumination
    3. sampling and reconstruction
    4. stochastic sampling

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    • (2024)N-Dimensional Gaussians for Fitting of High Dimensional FunctionsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657502(1-11)Online publication date: 13-Jul-2024
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