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A framework for GPU-accelerated exploration of massive time-varying rectilinear scalar volumes

Fabio Marton, Marco Agus, and Enrico Gobbetti

2019

Abstract

We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out-of-core representation, based on per-frame levels of hierarchically tiled non-redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low-bitrate codec able to store into fixed-size pages a variable-rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time-critical operations, while a near-lossless representation is employed to support high-quality static frame rendering. A flexible high-speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object-space and image-space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high-quality snapshots generated from near-lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi-billion-voxel frames.

Reference and download information

Fabio Marton, Marco Agus, and Enrico Gobbetti. A framework for GPU-accelerated exploration of massive time-varying rectilinear scalar volumes. Computer Graphics Forum, 38(3): 53-66, 2019. https://doi.org/10.1111/cgf.13671.

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Bibtex citation record

@Article{Marton:2019:FGE,
    author = {Fabio Marton and Marco Agus and Enrico Gobbetti},
    title = {A framework for GPU-accelerated exploration of massive time-varying rectilinear scalar volumes},
    journal = {Computer Graphics Forum},
    volume = {38},
    number = {3},
    pages = {53--66},
    year = {2019},
    abstract = { We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out-of-core representation, based on per-frame levels of hierarchically tiled non-redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low-bitrate codec able to store into fixed-size pages a variable-rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time-critical operations, while a near-lossless representation is employed to support high-quality static frame rendering. A flexible high-speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object-space and image-space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high-quality snapshots generated from near-lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi-billion-voxel frames. },
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Marton:2019:FGE'},
}