Multiple importance sampling characterization by weighted mean invariance

Mateu Sbert; Vlastimil Havran; László Szirmay-Kalos; Víctor Elvira

doi:10.1007/s00371-018-1522-x

Multiple importance sampling characterization by weighted mean invariance

Mateu Sbert, Vlastimil Havran^*, László Szirmay-Kalos, Víctor Elvira

^*Corresponding author for this work

School of Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we examine the linear combination of techniques and multiple importance sampling for Monte Carlo integration from a new perspective of quasi-arithmetic weighted means. The invariance property of these means allows us to define a new family of heuristics. We illustrate our results with several rendering examples, including environment mapping and path tracing.

Original language	English
Pages (from-to)	843-852
Number of pages	10
Journal	Visual Computer
Volume	34
Issue number	6-8
Early online date	3 May 2018
DOIs	https://doi.org/10.1007/s00371-018-1522-x
Publication status	Published - 1 Jun 2018

Keywords / Materials (for Non-textual outputs)

Global illumination
Monte Carlo
Multiple importance sampling
Rendering equation analysis

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10.1007/s00371-018-1522-x

Cite this

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title = "Multiple importance sampling characterization by weighted mean invariance",

abstract = "In this paper, we examine the linear combination of techniques and multiple importance sampling for Monte Carlo integration from a new perspective of quasi-arithmetic weighted means. The invariance property of these means allows us to define a new family of heuristics. We illustrate our results with several rendering examples, including environment mapping and path tracing.",

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AU - Sbert, Mateu

AU - Havran, Vlastimil

AU - Szirmay-Kalos, László

AU - Elvira, Víctor

PY - 2018/6/1

Y1 - 2018/6/1

N2 - In this paper, we examine the linear combination of techniques and multiple importance sampling for Monte Carlo integration from a new perspective of quasi-arithmetic weighted means. The invariance property of these means allows us to define a new family of heuristics. We illustrate our results with several rendering examples, including environment mapping and path tracing.

AB - In this paper, we examine the linear combination of techniques and multiple importance sampling for Monte Carlo integration from a new perspective of quasi-arithmetic weighted means. The invariance property of these means allows us to define a new family of heuristics. We illustrate our results with several rendering examples, including environment mapping and path tracing.

KW - Global illumination

KW - Monte Carlo

KW - Multiple importance sampling

KW - Rendering equation analysis

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DO - 10.1007/s00371-018-1522-x

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SP - 843

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JO - Visual Computer

JF - Visual Computer

IS - 6-8

ER -

Multiple importance sampling characterization by weighted mean invariance

Abstract

Keywords / Materials (for Non-textual outputs)

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