Near-Delaunay Metrics
We study metrics that assess how close a triangulation is to being a Delaunay triangulation, for use in contexts where a good triangulation is desired but constraints (e.g., maximum degree) prevent the use of the Delaunay triangulation itself. Our near-Delaunay metrics derive from common Delaunay pr...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
22.06.2021
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Subjects | |
Online Access | Get full text |
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Summary: | We study metrics that assess how close a triangulation is to being a Delaunay
triangulation, for use in contexts where a good triangulation is desired but
constraints (e.g., maximum degree) prevent the use of the Delaunay
triangulation itself. Our near-Delaunay metrics derive from common Delaunay
properties and satisfy a basic set of design criteria, such as being invariant
under similarity transformations. We compare the metrics, showing that each can
make different judgments as to which triangulation is closer to Delaunay. We
also present a preliminary experiment, showing how optimizing for these metrics
under different constraints gives similar, but not necessarily identical
results, on random and constructed small point sets. |
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DOI: | 10.48550/arxiv.2106.11621 |