Streaming compression of hexahedral meshes

Courbet, Clément; Isenburg, Martin

doi:10.1007/s00371-010-0481-7

Cited by 4 publications

(2 citation statements)

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“…A particular attention to I/O efficiency is thus required, to enable the encoding of huge meshes with a small memory footprint. Isenburg and coworkers are the first to propose streaming compression for VMs (extended from his method for triangular meshes [25]): for tetrahedral meshes [24], and then for hexahedral meshes [14]. In the latter, for instance, the compressor does not require the knowledge of the full list of vertices and cells before encoding.…”

Section: Single-rate Mesh Compressionmentioning

confidence: 99%

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

et al. 2019

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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 This work was partly presented in [43]. This is a post-peerreview, pre-copyedit version of an article published in Computational Geosciences. The final authenticated version is available online at: http://dx.

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Section: Single-rate Mesh Compressionmentioning

confidence: 99%

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

et al. 2019

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“…This framework has been the basis of several streaming compression techniques, such as Isenburg et al [2005c] for triangle meshes, Isenburg et al [2006] for tetrahedral meshes, and Courbet and Isenburg [2010] for hexahedral meshes. Compared to their nonstreaming counterparts, these techniques have generally equivalent geometry compression rates but worse connectivity compression rates.…”

Section: Handling Large Meshesmentioning

confidence: 99%

3D Mesh Compression

Maglo¹,

et al. 2015

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3D meshes are commonly used to represent virtual surface and volumes. However, their raw data representations take a large amount of space. Hence, 3D mesh compression has been an active research topic since the mid 1990s. In 2005, two very good review articles describing the pioneering works were published. Yet, new technologies have emerged since then. In this article, we summarize the early works and put the focus on these novel approaches. We classify and describe the algorithms, evaluate their performance, and provide synthetic comparisons. We also outline the emerging trends for future research.

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