Academic Journal
A linear heuristic for multiple importance sampling.
| Τίτλος: | A linear heuristic for multiple importance sampling. |
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| Συγγραφείς: | Sbert, Mateu, Szirmay-Kalos, László |
| Πηγή: | EURASIP Journal on Advances in Signal Processing; 3/7/2023, Vol. 2023 Issue 1, p1-18, 18p |
| Θεματικοί όροι: | Probability density function, Linear equations, Computer graphics, Heuristic, Sampling (Process) |
| Περίληψη: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Advances in Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1186/s13634-023-00990-8 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: A linear heuristic for multiple importance sampling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sbert%2C+Mateu%22">Sbert, Mateu</searchLink><br /><searchLink fieldCode="AR" term="%22Szirmay-Kalos%2C+László%22">Szirmay-Kalos, László</searchLink> – Name: TitleSource Label: Source Group: Src Data: EURASIP Journal on Advances in Signal Processing; 3/7/2023, Vol. 2023 Issue 1, p1-18, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+equations%22">Linear equations</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+graphics%22">Computer graphics</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+%28Process%29%22">Sampling (Process)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Advances in Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13634-023-00990-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Probability density function Type: general – SubjectFull: Linear equations Type: general – SubjectFull: Computer graphics Type: general – SubjectFull: Heuristic Type: general – SubjectFull: Sampling (Process) Type: general Titles: – TitleFull: A linear heuristic for multiple importance sampling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sbert, Mateu – PersonEntity: Name: NameFull: Szirmay-Kalos, László IsPartOfRelationships: – BibEntity: Dates: – D: 07 M: 03 Text: 3/7/2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 16876172 Numbering: – Type: volume Value: 2023 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Advances in Signal Processing Type: main |
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