Ternary Sparse Matrix Representation for Volumetric Mesh Subdivision and Processing on GPUs
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Date
2017
Journal Title
Journal ISSN
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
In this paper, we present a novel volumetric mesh representation suited for parallel computing on modern GPU architectures. The data structure is based on a compact, ternary sparse matrix storage of boundary operators. Boundary operators correspond to the first-order top-down relations of k-faces to their (k-1)-face facets. The compact, ternary matrix storage format is based on compressed sparse row matrices with signed indices and allows for efficient parallel computation of indirect and bottomup relations. This representation is then used in the implementation of several parallel volumetric mesh algorithms including Laplacian smoothing and volumetric Catmull-Clark subdivision. We compare these algorithms with their counterparts based on OpenVolumeMesh and achieve speedups from 3x to 531x, for sufficiently large meshes, while reducing memory consumption by up to 36%.
Description
@article{10.1111:cgf.13245,
journal = {Computer Graphics Forum},
title = {{Ternary Sparse Matrix Representation for Volumetric Mesh Subdivision and Processing on GPUs}},
author = {Mueller-Roemer, Johannes Sebastian and Altenhofen, Christian and Stork, André},
year = {2017},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13245}
}