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Geometry-aware metropolis light transport

Published: 04 December 2018 Publication History

Abstract

Markov chain Monte Carlo (MCMC) rendering utilizes a sequence of correlated path samples which is obtained by iteratively mutating the current state to the next. The efficiency of MCMC rendering depends on how well the mutation strategy is designed to adapt to the local structure of the state space. We present a novel MCMC rendering method that automatically adapts the step sizes of the mutations to the geometry of the rendered scene. Our geometry-aware path space perturbation largely avoids tentative samples with zero contribution due to occlusion. Our method limits the mutation step size by estimating the maximum opening angle of a cone, centered around a segment of a light transport path, where no geometry obstructs visibility. This geometry-aware mutation increases the acceptance rates, while not degrading the sampling quality. As this cone estimation introduces a considerable overhead if done naively, to make our approach efficient, we discuss and analyze fast approximate methods for cone angle estimation which utilize the acceleration structure already present for the ray-geometry intersection. Our new approach, integrated into the framework of Metropolis light transport, can achieve results with lower error and less artifact in equal time compared to current path space mutation techniques.

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  1. Geometry-aware metropolis light transport

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    Published In

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

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    Author Tags

    1. Markov chain Monte Carlo light transport
    2. global illumination

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    • (2023)Decorrelating ReSTIR Samplers via MCMC MutationsACM Transactions on Graphics10.1145/362916643:1(1-15)Online publication date: 19-Oct-2023
    • (2023)Manifold Path Guiding for Importance Sampling Specular ChainsACM Transactions on Graphics10.1145/361836042:6(1-14)Online publication date: 5-Dec-2023
    • (2022)Parameter-Free Single-Pass Parallel Metropolis Light Transport with Sensor Path VisibilityProceedings of the 2022 5th International Conference on Image and Graphics Processing10.1145/3512388.3512441(363-368)Online publication date: 7-Jan-2022
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    • (2020)Path differential-informed stratified MCMC and adaptive forward path samplingACM Transactions on Graphics10.1145/3414685.341785639:6(1-19)Online publication date: 27-Nov-2020
    • (2020)Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lightingACM Transactions on Graphics10.1145/3386569.339248139:4(148:1-148:17)Online publication date: 12-Aug-2020
    • (2020)Next Event Estimation++: Visibility Mapping for Efficient Light Transport SimulationComputer Graphics Forum10.1111/cgf.1413839:7(205-217)Online publication date: 24-Nov-2020

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