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. DOI: 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. }, doi = {10.1111/cgf.13671}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Marton:2019:FGE'}, }
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