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COVRA: A compression-domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks

Enrico Gobbetti, José Antonio Iglesias Guitián, and Fabio Marton

2012

Abstract

We present a novel multiresolution compression-domain GPU volume rendering architecture designed for interactive local and networked exploration of rectilinear scalar volumes on commodity platforms. In our approach, the volume is decomposed into a multiresolution hierarchy of bricks. Each brick is further subdivided into smaller blocks, which are compactly described by sparse linear combinations of prototype blocks stored in an overcomplete dictionary. The dictionary is learned, using limited computational and memory resources, by applying the K-SVD algorithm to a re-weighted non-uniformly sampled subset of the input volume, harnessing the recently introduced method of coresets. The result is a scalable high quality coding scheme, which allows very large volumes to be compressed off-line and then decom pressed on-demand during real-time GPU-accelerated rendering. Volumetric information can be maintained in compressed format through all the rendering pipeline. In order to efficiently support high quality filtering and shading, a specialized real-time renderer closely coordinates decompression with rendering, combining at each frame images produced by raycasting selectively decompressed portions of the current view- and transfer-function-dependent working set. The quality and performance of our approach is demonstrated on massive static and time-varying datasets.

Reference and download information

Enrico Gobbetti, José Antonio Iglesias Guitián, and Fabio Marton. COVRA: A compression-domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks. Computer Graphics Forum, 31(3pt4): 1315-1324, 2012. http://dx.doi.org/10.1111/j.1467-8659.2012.03124.x. Proc. Eurovis 2012.

Related multimedia productions

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Enrico Gobbetti, Jose A. Iglesias Guitian and Fabio Marton
COVRA: A compression-domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks
CRS4 Video n. 162 - Date: June 2012
In Computer Graphics Forum, 31, 2012. Proc. Eurovis 2012

Bibtex citation record

@Article{Gobbetti:2012:CCO,
    author = {Enrico Gobbetti and {Jos\'e Antonio} {Iglesias Guiti\'an} and Fabio Marton},
    title = {COVRA: A compression-domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks},
    journal = {Computer Graphics Forum},
    volume = {31},
    number = {3pt4},
    pages = {1315--1324},
    publisher = {Blackwell Publishing Ltd},
    year = {2012},
    issn = {1467-8659},
    abstract = { We present a novel multiresolution compression-domain GPU volume rendering architecture designed for interactive local and networked exploration of rectilinear scalar volumes on commodity platforms. In our approach, the volume is decomposed into a multiresolution hierarchy of bricks. Each brick is further subdivided into smaller blocks, which are compactly described by sparse linear combinations of prototype blocks stored in an overcomplete dictionary. The dictionary is learned, using limited computational and memory resources, by applying the K-SVD algorithm to a re-weighted non-uniformly sampled subset of the input volume, harnessing the recently introduced method of coresets. The result is a scalable high quality coding scheme, which allows very large volumes to be compressed off-line and then decom pressed on-demand during real-time GPU-accelerated rendering. Volumetric information can be maintained in compressed format through all the rendering pipeline. In order to efficiently support high quality filtering and shading, a specialized real-time renderer closely coordinates decompression with rendering, combining at each frame images produced by raycasting selectively decompressed portions of the current view- and transfer-function-dependent working set. The quality and performance of our approach is demonstrated on massive static and time-varying datasets. },
    note = {Proc. Eurovis 2012},
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Gobbetti:2012:CCO'},
}