A Unified Manifold Framework for Efficient BRDF Sampling based on Parametric Mixture Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10386558" target="_blank" >RIV/00216208:11320/18:10386558 - isvavai.cz</a>

Result on the web

<a href="https://cgg.mff.cuni.cz/~jaroslav/papers/2018-brdfmanifold/2018-herholz-brdfmanifold-paper.pdf" target="_blank" >https://cgg.mff.cuni.cz/~jaroslav/papers/2018-brdfmanifold/2018-herholz-brdfmanifold-paper.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2312/sre.20181171" target="_blank" >10.2312/sre.20181171</a>

Result language

angličtina

Original language name

A Unified Manifold Framework for Efficient BRDF Sampling based on Parametric Mixture Models

Original language description

VirtuallyallexistinganalyticBRDFmodelsarebuiltfrommultiplefunctionalcomponents(e.g.,Fresnelterm,normaldistribution function, etc.). This makes accurate importance sampling of the full model challenging, and so current solutions only cover a subset of the model&apos;s components. This leads to sub-optimal or even invalid proposed directional samples, which can negatively impact the efficiency of light transport solvers based on Monte Carlo integration. To overcome this problem, we propose a unified BRDF sampling strategy based on parametric mixture models (PMMs). We show that for a given BRDF, the parameters of the associated PMM can be defined in smooth manifold spaces, which can be compactly represented using multivariate B-Splines. These manifolds are defined in the parameter space of the BRDF and allow for arbitrary, continuous queries of the PMM representation for varying BRDF parameters, which further enables importance sampling for spatially varying BRDFs. Our representation is not limited to analytic BRDF models, but can also be used for sampling measured BRDF data. The resulting manifold framework enables accurate and efficient BRDF importance sampling with very small approximation errors.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA16-18964S" target="_blank" >GA16-18964S: Adaptive sampling and Markov chain Monte Carlo methods in light transport simulation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Eurographics Symposium on Rendering - Experimental Ideas &amp; Implementations

ISBN

978-3-03868-068-0

ISSN

1727-3463

e-ISSN

neuvedeno

Number of pages

12

Pages from-to

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Publisher name

The Eurographics Association

Place of publication

Switzerland

Event location

Karlsruhe, Germany

Event date

Jul 2, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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