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- W4206352693 abstract "This article proposes a Markov Chain Monte Carlo ( MCMC ) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes." @default.
- W4206352693 created "2022-01-25" @default.
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- W4206352693 date "2021-12-20" @default.
- W4206352693 modified "2023-09-27" @default.
- W4206352693 title "Ensemble Metropolis Light Transport" @default.
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- W4206352693 doi "https://doi.org/10.1145/3472294" @default.
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