Adaptively Layered Statistical Volumetric Obscurance

Quintjin Hendrickx, Leonardo Scandolo, Martin Eisemann, Elmar Eisemann

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 7th Conference on High-Performance Graphics
EditorsS Spencer
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)978-1-4503-3707-6
Publication statusPublished - 2015
EventHigh-Performance Graphics 2015 - Los Angeles, United States
Duration: 7 Aug 20159 Aug 2015

Publication series



ConferenceHigh-Performance Graphics 2015
Abbreviated titleHPG 2015
CountryUnited States
CityLos Angeles


  • Computer methodologies
  • Computer graphics
  • Image manipulation
  • Texturing

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  • Cite this

    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 (pp. 77-84). Association for Computing Machinery (ACM).