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A framework for GPU‐accelerated exploration of massive time‐varying rectilinear scalar volumes
Author(s) -
Marton Fabio,
Agus Marco,
Gobbetti Enrico
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13671
Subject(s) - computer science , rendering (computer graphics) , lossless compression , computer graphics (images) , frame rate , animation , cuda , parallel computing , computational science , algorithm , computer vision , data compression
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.