Path Space Markov Chain Monte Carlo Methods in Computer Graphics

Jakob, Wenzel

doi:10.1007/978-3-319-33507-0_4

Springer Proceedings in Mathematics &Amp; Statistics

2016

DOI: 10.1007/978-3-319-33507-0_4

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Path Space Markov Chain Monte Carlo Methods in Computer Graphics

Wenzel Jakob

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Cited by 2 publications

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A guided Monte Carlo radiative transfer method using mixture importance sampling

Zhang

2019

A&A

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In order to investigate the source of uncertainties for the Monte Carlo radiative transfer method, a path space formulation is proposed which expresses the integral form of the radiative transfer equation. It has been determined that some of the uncertainties depend on the sampling of photon propagation directions. To reduce this kind of uncertainty, we propose a guided Monte Carlo (GMC) method based on a direction mixture importance sampling strategy for simulating radiative transfer in a scattering medium. We validated the GMC method by implementing it in a backward Monte Carlo radiative transfer (BMCRT) code for the plane-parallel medium. Similar to the usual BMCRT method, the solution is determined by tracing photons from the detector towards the radiation source in the backward GMC method. Through test examples, we demonstrate the validity of the direction mixture importance sampling strategy and the GMC method.

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A guided Monte Carlo radiative transfer method using mixture importance sampling

Zhang

2019

A&A

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Survey of Markov Chain Monte Carlo Methods in Light Transport Simulation

Šik

Křivánek

2020

IEEE Trans. Visual. Comput. Graphics

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Path Space Markov Chain Monte Carlo Methods in Computer Graphics

Cited by 2 publications

References 24 publications

A guided Monte Carlo radiative transfer method using mixture importance sampling

A guided Monte Carlo radiative transfer method using mixture importance sampling

Survey of Markov Chain Monte Carlo Methods in Light Transport Simulation

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