Implementing data-dependent triangulations with higher order Delaunay triangulations
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessRestricted access - publisher's policy
The Delaunay triangulation is the standard choice for building triangulated irregular networks (TINs) to represent terrain surfaces. However, the Delaunay triangulation is based only on the 2D coordinates of the data points, ignoring their elevation. It has long been recognized that sometimes it may be beneficial to use other, non-Delaunay, criteria to build TINs. Data-dependent triangulations were introduced decades ago to address this. However, they are rarely used in practice, mostly because the optimization of data- dependent criteria often results in triangulations with many thin and elongated triangles. Recently, in the field of computational geometry, higher order Delaunay triangulations (HODTs) were introduced, trying to tackle both issues at the same time-data-dependent criteria and good triangle shape. Nevertheless, most previous studies about them have been limited to theoretical aspects. In this work we present the first extensive experimental study on the practical use of HODTs, as a tool to build data-dependent TINs. We present experiments with two USGS terrains that show that HODTs can give significant improvements over the Delaunay triangulation for the criteria identified as most important for data-dependent triangulations. The resulting triangulations have data-dependent values comparable to those obtained with pure data-dependent approaches, without compromising the shape of the triangles, and are faster to compute.
CitationRodríguez, N., Silveira, R.I. Implementing data-dependent triangulations with higher order Delaunay triangulations. A: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. "SIGSPATIAL'16 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Burlingame, CA, USA — October 31 - November 03, 2016". San Francisco: Association for Computing Machinery (ACM), 2016, p. 1-10.
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