This article may be too technical for most readers to understand.(June 2021) |
A triangulation of a set of points in the Euclidean space is a simplicial complex that covers the convex hull of , and whose vertices belong to . [1] In the plane (when is a set of points in ), triangulations are made up of triangles, together with their edges and vertices. Some authors require that all the points of are vertices of its triangulations. [2] In this case, a triangulation of a set of points in the plane can alternatively be defined as a maximal set of non-crossing edges between points of . In the plane, triangulations are special cases of planar straight-line graphs.
A particularly interesting kind of triangulations are the Delaunay triangulations. They are the geometric duals of Voronoi diagrams. The Delaunay triangulation of a set of points in the plane contains the Gabriel graph, the nearest neighbor graph and the minimal spanning tree of .
Triangulations have a number of applications, and there is an interest to find the "good" triangulations of a given point set under some criteria as, for instance minimum-weight triangulations. Sometimes it is desirable to have a triangulation with special properties, e.g., in which all triangles have large angles (long and narrow ("splinter") triangles are avoided). [3]
Given a set of edges that connect points of the plane, the problem to determine whether they contain a triangulation is NP-complete. [4]
Some triangulations of a set of points can be obtained by lifting the points of into (which amounts to add a coordinate to each point of ), by computing the convex hull of the lifted set of points, and by projecting the lower faces of this convex hull back on . The triangulations built this way are referred to as the regular triangulations of . When the points are lifted to the paraboloid of equation , this construction results in the Delaunay triangulation of . Note that, in order for this construction to provide a triangulation, the lower convex hull of the lifted set of points needs to be simplicial. In the case of Delaunay triangulations, this amounts to require that no points of lie in the same sphere.
Every triangulation of any set of points in the plane has triangles and edges where is the number of points of in the boundary of the convex hull of . This follows from a straightforward Euler characteristic argument. [5]
Triangle Splitting Algorithm : Find the convex hull of the point set and triangulate this hull as a polygon. Choose an interior point and draw edges to the three vertices of the triangle that contains it. Continue this process until all interior points are exhausted. [6]
Incremental Algorithm : Sort the points of according to x-coordinates. The first three points determine a triangle. Consider the next point in the ordered set and connect it with all previously considered points which are visible to p. Continue this process of adding one point of at a time until all of has been processed. [7]
The following table reports time complexity results for the construction of triangulations of point sets in the plane, under different optimality criteria, where is the number of points.
minimize | maximize | ||
---|---|---|---|
minimum | angle | (Delaunay triangulation) | |
maximum | [8] [9] | ||
minimum | area | [10] | [11] |
maximum | [11] | ||
maximum | degree | NP-complete for degree of 7 [12] | |
maximum | eccentricity | [9] | |
minimum | edge length | (Closest pair of points problem) | NP-complete [13] |
maximum | [14] | (using the Convex hull) | |
sum of | NP-hard (Minimum-weight triangulation) | ||
minimum | height | [9] | |
maximum | slope | [9] |
In computational geometry, a Delaunay triangulation or Delone triangulation of a set of points in the plane subdivides their convex hull into triangles whose circumcircles do not contain any of the points. This maximizes the size of the smallest angle in any of the triangles, and tends to avoid sliver triangles.
In geometry, the convex hull, convex envelope or convex closure of a shape is the smallest convex set that contains it. The convex hull may be defined either as the intersection of all convex sets containing a given subset of a Euclidean space, or equivalently as the set of all convex combinations of points in the subset. For a bounded subset of the plane, the convex hull may be visualized as the shape enclosed by a rubber band stretched around the subset.
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In discrete geometry, a -set of a finite point set in the Euclidean plane is a subset of elements of that can be strictly separated from the remaining points by a line. More generally, in Euclidean space of higher dimensions, a -set of a finite point set is a subset of elements that can be separated from the remaining points by a hyperplane. In particular, when , the line or hyperplane that separates a -set from the rest of is a halving line or halving plane.
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In computational geometry and computer science, the minimum-weight triangulation problem is the problem of finding a triangulation of minimal total edge length. That is, an input polygon or the convex hull of an input point set must be subdivided into triangles that meet edge-to-edge and vertex-to-vertex, in such a way as to minimize the sum of the perimeters of the triangles. The problem is NP-hard for point set inputs, but may be approximated to any desired degree of accuracy. For polygon inputs, it may be solved exactly in polynomial time. The minimum weight triangulation has also sometimes been called the optimal triangulation.
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In computational geometry, an alpha shape, or α-shape, is a family of piecewise linear simple curves in the Euclidean plane associated with the shape of a finite set of points. They were first defined by Edelsbrunner, Kirkpatrick & Seidel (1983). The alpha-shape associated with a set of points is a generalization of the concept of the convex hull, i.e. every convex hull is an alpha-shape but not every alpha shape is a convex hull.
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