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.
|Status||Gepubliceerd - 10 aug. 2021|
|Evenement||33rd Canadian Conference on Computational Geometry (CCCG 2021) - |
Duur: 10 aug. 2021 → 12 aug. 2021
|Congres||33rd Canadian Conference on Computational Geometry (CCCG 2021)|
|Periode||10/08/21 → 12/08/21|