Adaptive multiple importance sampling for general functions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00312276" target="_blank" >RIV/68407700:21230/17:00312276 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00371-017-1398-1" target="_blank" >https://link.springer.com/article/10.1007/s00371-017-1398-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00371-017-1398-1" target="_blank" >10.1007/s00371-017-1398-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive multiple importance sampling for general functions

Popis výsledku v původním jazyce

We propose a mathematical expression for the optimal distribution of the number of samples in multiple importance sampling (MIS) and also give heuristics that work well in practice. The MIS balance heuristic is based on weighting several sampling techniques into a single estimator, and it is equal to Monte Carlo integration using a mixture of distributions. The MIS balance heuristic has been used since its invention almost exclusively with an equal number of samples from each technique. We introduce the sampling costs and adapt the formulae to work well with them. We also show the relationship between the MIS balance heuristic and the linear combination of these techniques, and that MIS balance heuristic minimum variance is always less or equal than the minimum variance of the independent techniques. Finally, we give one-dimensional and two-dimensional function examples, including an environment map illumination computation with occlusion.

Název v anglickém jazyce

Adaptive multiple importance sampling for general functions

Popis výsledku anglicky

We propose a mathematical expression for the optimal distribution of the number of samples in multiple importance sampling (MIS) and also give heuristics that work well in practice. The MIS balance heuristic is based on weighting several sampling techniques into a single estimator, and it is equal to Monte Carlo integration using a mixture of distributions. The MIS balance heuristic has been used since its invention almost exclusively with an equal number of samples from each technique. We introduce the sampling costs and adapt the formulae to work well with them. We also show the relationship between the MIS balance heuristic and the linear combination of these techniques, and that MIS balance heuristic minimum variance is always less or equal than the minimum variance of the independent techniques. Finally, we give one-dimensional and two-dimensional function examples, including an environment map illumination computation with occlusion.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA14-19213S" target="_blank" >GA14-19213S: Mobilní měření, komprese a syntéza obrazu pro prostorově proměnnou reflektanci materiálů</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

The Visual Computer

ISSN

0178-2789

e-ISSN

1432-2315

Svazek periodika

33

Číslo periodika v rámci svazku

6-8

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

11

Strana od-do

845-855

Kód UT WoS článku

000402964800016

EID výsledku v databázi Scopus

2-s2.0-85019082822