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    Efficient GPU Based Sampling for Scene-Space Video Processing

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    Date
    2015
    Author
    Klose, Felix
    Wang, Oliver
    Bazin, Jean-Charles
    Magnor, Marcus
    Sorkine-Hornung, Alexander
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    Abstract
    We describe a method to efficiently collect and filter a large set of 2D pixel observations of unstructured 3D points, with applications to scene-space aware video processing. One of the main challenges in scene-space video processing is to achieve reasonable computation time despite the very large volumes of data, often in the order of billions of pixels. The bottleneck is determining a suitable set of candidate samples used to compute each output video pixel color. These samples are observations of the same 3D point, and must be gathered from a large number of candidate pixels, by volumetric 3D queries in scene-space. Our approach takes advantage of the spatial and temporal continuity inherent to video to greatly reduce the candidate set of samples by solving 3D volumetric queries directly on a series of 2D projections, using out-of-core data streaming and an efficient GPU producerconsumer scheme that maximizes hardware utilization by exploiting memory locality. Our system is capable of processing over a trillion pixel samples, enabling various scene-space video processing applications on full HD video output with hundreds of frames and processing times in the order of a few minutes.
    BibTeX
    @inproceedings {10.2312:vmv.20151264,
    booktitle = {Vision, Modeling & Visualization},
    editor = {David Bommes and Tobias Ritschel and Thomas Schultz},
    title = {{Efficient GPU Based Sampling for Scene-Space Video Processing}},
    author = {Klose, Felix and Wang, Oliver and Bazin, Jean-Charles and Magnor, Marcus and Sorkine-Hornung, Alexander},
    year = {2015},
    publisher = {The Eurographics Association},
    ISBN = {978-3-905674-95-8},
    DOI = {10.2312/vmv.20151264}
    }
    URI
    http://dx.doi.org/10.2312/vmv.20151264
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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