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

  • Jean-Luc Peyrot
  • Laurent DuvalEmail author
  • Frédéric Payan
  • Lauriane Bouard
  • Lénaïc Chizat
  • Sébastien Schneider
  • Marc Antonini
Open Access
Original Paper

Abstract

With huge data acquisition progresses realized in the past decades and acquisition systems now able to produce high resolution grids and point clouds, the digitization of physical terrains becomes increasingly more precise. Such extreme quantities of generated and modeled data greatly impact computational performances 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 and storage are thus becoming “enabling technologies” in HPC experimental and simulation science. We propose HexaShrink, an original decomposition scheme for structured hexahedral volume meshes. The latter are used for instance in biomedical engineering, materials science, or geosciences. HexaShrink provides a comprehensive framework allowing efficient mesh visualization and storage. Its exactly reversible multiresolution decomposition yields a hierarchy of meshes of increasing levels of details, in terms of either geometry, continuous or categorical properties of cells. Starting with an overview of volume meshes compression techniques, our contribution blends coherently different multiresolution wavelet schemes in different dimensions. It results in a global framework preserving discontinuities (faults) across scales, implemented as a fully reversible upscaling at different resolutions. Experimental results are provided on meshes of varying size and complexity. They emphasize the consistency 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 compression techniques.

Keywords

Compression Corner point grid Discrete wavelet transform Geometrical discontinuities Hexahedral volume meshes High-performance computing Multiscale methods Simulation Upscaling 

Notes

Acknowledgments

The authors would like to thank C. Dawson and M. F. Wheeler for their help, as well as the reviewers whose comments helped improve the compression performance assessment and comparison.

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© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.IFP Energies nouvellesSolaizeFrance
  2. 2.IFP Energies nouvellesRueil-MalmaisonFrance
  3. 3.ESIEE ParisUniversity Paris-Est, LIGMNoisy-le-GrandFrance
  4. 4.CNRS, I3SUniversité Côte d’AzurSophia AntipolisFrance
  5. 5.INRIA, ENSPSL Research University ParisParisFrance
  6. 6.HoloMakeMeudonFrance

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