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With huge data acquisition progresses realized in the past decades andacquisition systems now able to produce high resolution grids and point clouds,the digitization of physical terrains becomes increasingly more precise. Suchextreme quantities of generated and modeled data greatly impact computationalperformances on many levels of high-performance computing (HPC): storage media,memory requirements, transfer capability, and finally simulation interactivity,necessary to exploit this instance of big data. Efficient representations andstorage are thus becoming "enabling technologies'' in HPC experimental andsimulation science. We propose HexaShrink, an original decomposition scheme forstructured hexahedral volume meshes. The latter are used for instance inbiomedical engineering, materials science, or geosciences. HexaShrink providesa comprehensive framework allowing efficient mesh visualization and storage.Its exactly reversible multiresolution decomposition yields a hierarchy ofmeshes of increasing levels of details, in terms of either geometry, continuousor categorical properties of cells. Starting with an overview of volume meshescompression techniques, our contribution blends coherently differentmultiresolution wavelet schemes in different dimensions. It results in a globalframework preserving discontinuities (faults) across scales, implemented as afully reversible upscaling at different resolutions. Experimental results areprovided on meshes of varying size and complexity. They emphasize theconsistency of the proposed representation, in terms of visualization,attribute downsampling and distribution at different resolutions. Finally,HexaShrink yields gains in storage space when combined to lossless compressiontechniques.

computational geosciences

HexaShrink, an exact scalable framework for hexahedral meshes with  attributes and discontinuities: multiresolution rendering and storage of  geoscience models