Product Importance Sampling for Light Transport Path Guiding

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10336431" target="_blank" >RIV/00216208:11320/16:10336431 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1111/cgf.12950" target="_blank" >http://dx.doi.org/10.1111/cgf.12950</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/cgf.12950" target="_blank" >10.1111/cgf.12950</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Product Importance Sampling for Light Transport Path Guiding

Popis výsledku v původním jazyce

The efficiency of Monte Carlo algorithms for light transport simulation is directly related to their ability to importance-sample the product of the illumination and reflectance in the rendering equation. Since the optimal sampling strategy would require knowledge about the transport solution itself, importance sampling most often follows only one of the known factors - BRDF or an approximation of the incident illumination. To address this issue, we propose to represent the illumination and the reflectance factors by the Gaussian mixture model (GMM), which we fit by using a combination of weighted expectation maximization and non-linear optimization methods. The GMM representation then allows us to obtain the resulting product distribution for importance sampling on-the-fly at each scene point. For its efficient evaluation and sampling we preform an up-front adaptive decimation of both factor mixtures. In comparison to state-of-the-art sampling methods, we show that our product importance sampling can lead to significantly better convergence in scenes with complex illumination and reflectance.

Název v anglickém jazyce

Product Importance Sampling for Light Transport Path Guiding

Popis výsledku anglicky

The efficiency of Monte Carlo algorithms for light transport simulation is directly related to their ability to importance-sample the product of the illumination and reflectance in the rendering equation. Since the optimal sampling strategy would require knowledge about the transport solution itself, importance sampling most often follows only one of the known factors - BRDF or an approximation of the incident illumination. To address this issue, we propose to represent the illumination and the reflectance factors by the Gaussian mixture model (GMM), which we fit by using a combination of weighted expectation maximization and non-linear optimization methods. The GMM representation then allows us to obtain the resulting product distribution for importance sampling on-the-fly at each scene point. For its efficient evaluation and sampling we preform an up-front adaptive decimation of both factor mixtures. In comparison to state-of-the-art sampling methods, we show that our product importance sampling can lead to significantly better convergence in scenes with complex illumination and reflectance.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GA16-18964S" target="_blank" >GA16-18964S: Adaptivní vzorkování a metody Markov chain Monte Carlo v simulaci transportu světla</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Computer Graphics Forum [online]

ISSN

1467-8659

e-ISSN

—

Svazek periodika

35

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

67-77

Kód UT WoS článku

000383444100008

EID výsledku v databázi Scopus

2-s2.0-84983314451