A Dimension-Independent Data Structure forSimplicial Complexes
Leila De Floriani, Annie Hui, Daniele Panozzo, and David Canino
Proceedings, 19th International Meshing Roundtable, Springer-Verlag, pp.403-420, October 3-6 2010
19th International Meshing Roundtable
Chattanooga, Tennessee, USA.
October 3-6, 2010
Department of Computer Science, University of Genova, Italy.
Email: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Department of Computer Science, University of Maryland, MD, USA.
We consider here the problem of representing non-manifold shapes discretized
as d-dimensional simplicial Euclidean complexes. To this aim, we propose a
dimension-independent data structure for simplicial complexes, called the Incidence
Simplicial (IS) data structure, which is scalable to manifold complexes, and supports
efficient navigation and topological modifications. The IS data structure has
the same expressive power and exibits performances in query and update operations
as the incidence graph, a widely-used representation for general cell complexes, but
it is much more compact. Here, we describe the IS data structure and we evaluate its
storage cost. Moreover, we present efficient algorithms for navigating and for generating
a simplicial complex described as an IS data structure. We compare the IS
data structure with the incidence graph and with dimension-specific representations
for simplicial complexes.
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