IA*: An adjacency-based representation for non-manifold simplicial shapes in arbitrary dimensions
✍ Scribed by David Canino; Leila De Floriani; Kenneth Weiss
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 249 KB
- Volume
- 35
- Category
- Article
- ISSN
- 0097-8493
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✦ Synopsis
We propose a compact, dimension-independent data structure for manifold, non-manifold and nonregular simplicial complexes, that we call the Generalized Indexed Data Structure with Adjacencies (IA n data structure). It encodes only top simplices, i.e. the ones that are not on the boundary of any other simplex, plus a suitable subset of the adjacency relations. We describe the IA n data structure in arbitrary dimensions, and compare the storage requirements of its 2D and 3D instances with both dimension-specific and dimension-independent representations. We show that the IA n data structure is more cost effective than other dimension-independent representations and is even slightly more compact than the existing dimension-specific ones. We present efficient algorithms for navigating a simplicial complex described as an IA n data structure. This shows that the IA n data structure allows retrieving all topological relations of a given simplex by considering only its local neighborhood and thus it is a more efficient alternative to incidence-based representations when information does not need to be encoded for boundary simplices.