Discovering New Monte Carlo Noise Filters with Genetic Programming
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- @InProceedings{conf/eurographics/KanDK17,
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author = "Peter Kan and Maxim Davletaliyev and Hannes Kaufmann",
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title = "Discovering New Monte Carlo Noise Filters with Genetic
Programming",
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booktitle = "Eurographics (Short Papers)",
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editor = "Adrien Peytavie and Carles Bosch",
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publisher = "Eurographics Association",
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year = "2017",
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pages = "25--28",
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address = "Lyon, France",
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month = apr # " 24-28",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1017-4656",
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bibdate = "2018-07-19",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurographics/eg-short2017.html#KanDK17",
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URL = "http://diglib.eg.org/handle/10.2312/2631250",
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DOI = "doi:10.2312/egsh.20171006",
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abstrct = "This paper presents a novel method for the discovery
of new analytical filters suitable for filtering of
noise in Monte Carlo rendering. Our method uses genetic
programming to evolve the set of analytical filtering
expressions with the goal to minimize image error in
training scenes. We show that genetic programming is
capable of learning new filtering expressions with
quality comparable to state of the art noise filters in
Monte Carlo rendering. Additionally, the analytical
nature of the resulting expressions enables the
run-times one order of magnitude faster than compared
state of the art methods. Finally, we present a new
analytical filter discovered by our method which is
suitable for filtering of Monte Carlo noise in diffuse
scenes.",
- }
Genetic Programming entries for
Peter Kan
Maxim Davletaliyev
Hannes Kaufmann
Citations