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Fusing state spaces for markov chain Monte Carlo rendering

Published: 20 July 2017 Publication History

Abstract

Rendering algorithms using Markov chain Monte Carlo (MCMC) currently build upon two different state spaces. One of them is the path space, where the algorithms operate on the vertices of actual transport paths. The other state space is the primary sample space, where the algorithms operate on sequences of numbers used for generating transport paths. While the two state spaces are related by the sampling procedure of transport paths, all existing MCMC rendering algorithms are designed to work within only one of the state spaces. We propose a first framework which provides a comprehensive connection between the path space and the primary sample space. Using this framework, we can use mutation strategies designed for one space with mutation strategies in the respective other space. As a practical example, we take a combination of manifold exploration and multiplexed Metropolis light transport using our framework. Our results show that the simultaneous use of the two state spaces improves the robustness of MCMC rendering. By combining efficient local exploration in the path space with global jumps in primary sample space, our method achieves more uniform convergence as compared to using only one space.

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    cover image ACM Transactions on Graphics
    ACM Transactions on Graphics  Volume 36, Issue 4
    August 2017
    2155 pages
    ISSN:0730-0301
    EISSN:1557-7368
    DOI:10.1145/3072959
    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: 20 July 2017
    Published in TOG Volume 36, Issue 4

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    Author Tags

    1. global illumination
    2. markov chain Monte Carlo

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    • (2024)ReSTIR Subsurface Scattering for Real-Time Path TracingProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36753727:3(1-19)Online publication date: 9-Aug-2024
    • (2023)Langevin dynamics in stochastic ray tracing: computing the preconditioning matrix according to restrictions and choice of time stepKeldysh Institute Preprints10.20948/prepr-2023-63(1-26)Online publication date: 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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    • (2021)Light Transport in Realistic Rendering: State-of-the-Art Simulation MethodsProgramming and Computing Software10.1134/S036176882104003447:4(298-326)Online publication date: 1-Jul-2021
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    • (2020)Survey of Markov Chain Monte Carlo Methods in Light Transport SimulationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.288045526:4(1821-1840)Online publication date: 1-Apr-2020
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