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    Deep Partitioned Shadow Volumes Using Stackless and Hybrid Traversals

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    Date
    2016
    Author
    Mora, Frédéric
    Gerhards, Julien
    Aveneau, Lilian
    Ghazanfarpour, Djamchid
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    Abstract
    Computing accurate hard shadows is a difficult problem in interactive rendering. Previous methods rely either on Shadow Maps or Shadow Volumes. Recently Partitioned Shadow Volumes (PSV) has been introduced. It revisits the old Shadow Volumes Binary Tree Space Partitioning algorithm, leading to a practicable and efficient technique. In this article, we analyze the PSV query algorithm and identify two main drawbacks: First, it uses a stack which is not GPU friendly; its size must be small enough to reduce the register pressure, but large enough to avoid stack overflow. Second, PSV struggles with configurations involving significant depth complexity, especially for lit points. We solve these problems by adding a depth information to the PSV data structure, and by designing a stackless query. In addition, we show how to combine the former PSV query with our stackless solution, leading to a hybrid technique taking advantage of both. This eliminates any risk of stack overflow, and our experiments demonstrate that these improvements accelerate the rendering time up to a factor of 3.
    BibTeX
    @inproceedings {10.2312:sre.20161212,
    booktitle = {Eurographics Symposium on Rendering - Experimental Ideas & Implementations},
    editor = {Elmar Eisemann and Eugene Fiume},
    title = {{Deep Partitioned Shadow Volumes Using Stackless and Hybrid Traversals}},
    author = {Mora, Frédéric and Gerhards, Julien and Aveneau, Lilian and Ghazanfarpour, Djamchid},
    year = {2016},
    publisher = {The Eurographics Association},
    ISSN = {1727-3463},
    ISBN = {978-3-03868-019-2},
    DOI = {10.2312/sre.20161212}
    }
    URI
    http://dx.doi.org/10.2312/sre.20161212
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA