Abstract

Multiple importance sampling combines the probability density functions of several sampling techniques into an importance function. The combination weights are the proportion of samples used for the particular techniques. This paper addresses the determination of the optimal combination weights from a few initial samples. Instead of the numerically unstable optimization of the variance, in our solution the quasi-optimal weights are obtained by solving a linear equation, which leads to simpler computations and more robust estimations. The proposed method is validated with 1D numerical examples and with the direct lighting problem of computer graphics.

Details

Title
A linear heuristic for multiple importance sampling
Author
Sbert, Mateu 1   VIAFID ORCID Logo  ; Szirmay-Kalos, László 2 

1  University of Girona, Department of Informatics, Applied Mathematics and Statistics, Girona, Spain (GRID:grid.5319.e) (ISNI:0000 0001 2179 7512) 
2  Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Budapest, Hungary (GRID:grid.6759.d) (ISNI:0000 0001 2180 0451) 
Pages
31
Publication year
2023
Publication date
Dec 2023
Publisher
Springer Nature B.V.
ISSN
16876172
e-ISSN
16876180
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1186/s13634-023-00990-8
ProQuest document ID
2784119090
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.