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In Proceedings of High Performance Graphics

Adaptively layered statistical volumetric obscurance

Quintijn Hendrickx, Leonardo Scandolo, Martin Eisemann, and Elmar Eisemann

Depth Layering: While a single layer will result in a single average over all geometry (a), we can slice our scene in multiple depth layers to obtain averages for each layer separately (b).

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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.

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Quintijn Hendrickx, Leonardo Scandolo, Martin Eisemann, and Elmar Eisemann, Adaptively layered statistical volumetric obscurance, In Proceedings of High Performance Graphics, pp. 77–84, 2015.

BibTex 

@inproceedings{bib:hendrickx:2015,
    author       = { Hendrickx, Quintijn and Scandolo, Leonardo and Eisemann, Martin and Eisemann, Elmar },    
    title        = { Adaptively layered statistical volumetric obscurance },
    booktitle    = { In Proceedings of High Performance Graphics },
    year         = { 2015 },
    pages        = { 77--84 },
    doi          = { 10.1145/2790060.2790070 },
    dblp         = { conf/egh/HendrickxSEE15 },
    url          = { https://publications.graphics.tudelft.nl/papers/253 },
}