Adaptively Layered Statistical Volumetric Obscurance

Quintjin Hendrickx; Leonardo Scandolo; Martin Eisemann; Elmar Eisemann

doi:10.1145/2790060.2790070

Adaptively Layered Statistical Volumetric Obscurance

Quintjin Hendrickx, Leonardo Scandolo, Martin Eisemann, Elmar Eisemann

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

Abstract

We accelerate volumetric obscurance, a variant of ambient occlusion, and solve undersampling artifacts, such as banding, noise or blurring, that screen-space techniques traditionally suffer from. We make use of an efficient statistical model to evaluate the occlusion factor in screen-space using a single sample. Overestimations and halos are reduced by an adaptive layering of the visible geometry. Bias at tilted surfaces is avoided by projecting and evaluating
the volumetric obscurance in tangent space of each surface point. We compare our approach to several traditional screen-space ambient obscurance techniques and show its competitive qualitative and quantitative performance. Our algorithm maps well to graphics hardware, does not require the traditional bilateral blur step of previous approaches, and avoids typical screen-space related artifacts
such as temporal instability due to undersampling.

Original language	English
Title of host publication	Proceedings of the 7th Conference on High-Performance Graphics
Editors	S Spencer
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	77-84
Number of pages	8
ISBN (Print)	978-1-4503-3707-6
DOIs	https://doi.org/10.1145/2790060.2790070
Publication status	Published - 2015
Event	High-Performance Graphics 2015 - Los Angeles, United States Duration: 7 Aug 2015 → 9 Aug 2015

Publication series

Name
Publisher	ACM

Conference

Conference	High-Performance Graphics 2015
Abbreviated title	HPG 2015
Country/Territory	United States
City	Los Angeles
Period	7/08/15 → 9/08/15

Keywords

Computer methodologies
Computer graphics
Image manipulation
Texturing

Access to Document

10.1145/2790060.2790070

Cite this

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title = "Adaptively Layered Statistical Volumetric Obscurance",

abstract = "We accelerate volumetric obscurance, a variant of ambient occlusion, and solve undersampling artifacts, such as banding, noise or blurring, that screen-space techniques traditionally suffer from. We make use of an efficient statistical model to evaluate the occlusion factor in screen-space using a single sample. Overestimations and halos are reduced by an adaptive layering of the visible geometry. Bias at tilted surfaces is avoided by projecting and evaluatingthe volumetric obscurance in tangent space of each surface point. We compare our approach to several traditional screen-space ambient obscurance techniques and show its competitive qualitative and quantitative performance. Our algorithm maps well to graphics hardware, does not require the traditional bilateral blur step of previous approaches, and avoids typical screen-space related artifactssuch as temporal instability due to undersampling.",

keywords = "Computer methodologies, Computer graphics, Image manipulation, Texturing",

author = "Quintjin Hendrickx and Leonardo Scandolo and Martin Eisemann and Elmar Eisemann",

year = "2015",

doi = "10.1145/2790060.2790070",

language = "English",

isbn = "978-1-4503-3707-6",

publisher = "Association for Computing Machinery (ACM)",

pages = "77--84",

editor = "S Spencer",

booktitle = "Proceedings of the 7th Conference on High-Performance Graphics",

address = "United States",

note = "High-Performance Graphics 2015, HPG 2015 ; Conference date: 07-08-2015 Through 09-08-2015",

}

Hendrickx, Q, Scandolo, L, Eisemann, M & Eisemann, E 2015, Adaptively Layered Statistical Volumetric Obscurance. in S Spencer (ed.), Proceedings of the 7th Conference on High-Performance Graphics. Association for Computing Machinery (ACM), New York, NY, pp. 77-84, High-Performance Graphics 2015, Los Angeles, United States, 7/08/15. https://doi.org/10.1145/2790060.2790070

Adaptively Layered Statistical Volumetric Obscurance. / Hendrickx, Quintjin; Scandolo, Leonardo; Eisemann, Martin et al.
Proceedings of the 7th Conference on High-Performance Graphics. ed. / S Spencer. New York, NY: Association for Computing Machinery (ACM), 2015. p. 77-84.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Adaptively Layered Statistical Volumetric Obscurance

AU - Hendrickx, Quintjin

AU - Scandolo, Leonardo

AU - Eisemann, Martin

AU - Eisemann, Elmar

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AB - We accelerate volumetric obscurance, a variant of ambient occlusion, and solve undersampling artifacts, such as banding, noise or blurring, that screen-space techniques traditionally suffer from. We make use of an efficient statistical model to evaluate the occlusion factor in screen-space using a single sample. Overestimations and halos are reduced by an adaptive layering of the visible geometry. Bias at tilted surfaces is avoided by projecting and evaluatingthe volumetric obscurance in tangent space of each surface point. We compare our approach to several traditional screen-space ambient obscurance techniques and show its competitive qualitative and quantitative performance. Our algorithm maps well to graphics hardware, does not require the traditional bilateral blur step of previous approaches, and avoids typical screen-space related artifactssuch as temporal instability due to undersampling.

KW - Computer methodologies

KW - Computer graphics

KW - Image manipulation

KW - Texturing

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M3 - Conference contribution

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BT - Proceedings of the 7th Conference on High-Performance Graphics

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CY - New York, NY

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Adaptively Layered Statistical Volumetric Obscurance

Abstract

Publication series

Conference

Keywords

Access to Document

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