dc.contributor.author	Kán, Peter	en_US
dc.contributor.author	Davletaliyev, Maxim	en_US
dc.contributor.author	Kaufmann, Hannes	en_US
dc.contributor.editor	Adrien Peytavie and Carles Bosch	en_US
dc.date.accessioned	2017-04-22T16:47:00Z
dc.date.available	2017-04-22T16:47:00Z
dc.date.issued	2017
dc.description.abstract	This paper presents a novel method for the discovery of new analytical filters suitable for filtering of noise in Monte Carlo rendering. Our method utilizes 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.	en_US
dc.description.sectionheaders	Lighting and Rendering
dc.description.seriesinformation	EG 2017 - Short Papers
dc.identifier.doi	10.2312/egsh.20171006
dc.identifier.issn	1017-4656
dc.identifier.pages	25-28
dc.identifier.uri	https://doi.org/10.2312/egsh.20171006
dc.identifier.uri	https://diglib.eg.org:443/handle/10.2312/egsh20171006
dc.publisher	The Eurographics Association	en_US
dc.subject	I.3.7 [Computer Graphics]
dc.subject	3D Graphics and Realism
dc.subject	Raytracing
dc.title	Discovering New Monte Carlo Noise Filters with Genetic Programming	en_US

Discovering New Monte Carlo Noise Filters with Genetic Programming

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