Adaptively Layered Statistical Volumetric Obscurance

Conference paper (2015)

Authors

Quintjin Hendrickx External organisation
L. Scandolo Computer Graphics and Visualisation - EEMCS
M. Eisemann Pattern Recognition and Bioinformatics - EEMCS , TH Köln
E. Eisemann Computer Graphics and Visualisation - EEMCS
Research Group
Computer Graphics and Visualisation (EEMCS) (TU Delft)
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Published Date

2015

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Computer Graphics and Visualisation

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.