\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.13 - 3D Periodic Triangulations
CGAL::Periodic_3_triangulation_hierarchy_3< PTr > Class Template Reference

#include <CGAL/Periodic_3_triangulation_hierarchy_3.h>

Inherits from



The class Periodic_3_triangulation_hierarchy_3 implements a triangulation augmented with a data structure which allows fast point location queries.

Template Parameters

Template Parameters
PTrmust be one of the CGAL periodic triangulation classes. In the current implementation, only Periodic_3_Delaunay_triangulation_3 is supported for.

PTr::Vertex has to be a model of the concept Periodic_3TriangulationHierarchyVertexBase_3.

PTr::Geom_traits has to be a model of the concept Periodic_3DelaunayTriangulationTraits_3.

Periodic_3_triangulation_hierarchy_3 offers exactly the same functionalities as PTr. Most of these functionalities (point location, insertion, removal \( \ldots\) ) are overloaded to improve their efficiency by using the hierarchic structure.

Note that, since the algorithms that are provided are randomized, the running time of constructing a triangulation with a hierarchy may be improved when shuffling the data points.

However, the I/O operations are not overloaded. Thus, writing a hierarchy into a file will lose the hierarchic structure and reading it from the file will result in an ordinary triangulation whose efficiency will be the same as PTr.


The data structure is a hierarchy of triangulations. The triangulation at the lowest level is the original triangulation where operations and point location are to be performed. Then at each succeeding level, the data structure stores a triangulation of a small random sample of the vertices of the triangulation at the preceding level. Point location is done through a top-down nearest neighbor query. The nearest neighbor query is first performed naively in the top level triangulation. Then, at each following level, the nearest neighbor at that level is found through a linear walk performed from the nearest neighbor found at the preceding level. Because the number of vertices in each triangulation is only a small fraction of the number of vertices of the preceding triangulation the data structure remains small and achieves fast point location queries on real data.

See also