Delaunay triangulation

The Delaunay triangulation with all the circumcircles and their centers (in red).

Connecting the centers of the circumcircles produces the Voronoi diagram (in red).

The Delaunay triangulation of a discrete point set P in general position corresponds to the dual graph of the Voronoi diagram for P. The circumcenters of Delaunay triangles are the vertices of the Voronoi diagram. In the 2D case, the Voronoi vertices are connected via edges, that can be derived from adjacency-relationships of the Delaunay triangles: If two triangles share an edge in the Delaunay triangulation, their circumcenters are to be connected with an edge in the Voronoi tesselation.

Special cases where this relationship does not hold, or is ambiguous, include cases like:

• Three or more collinear points, where the circumcircles are of infinite radii.
• Four or more points on a perfect circle, where the triangulation is ambiguous and all circumcenters are trivially identical.
• Edges of the Voronoi diagram going to infinity are not defined by this relation in case of a finite set P. If the Delaunay triangulation is calculated using the Bowyer–Watson algorithm then the circumcenters of triangles having a common vertex with the "super" triangle should be ignored. Edges going to infinity start from a circumcenter and they are perpendicular to the common edge between the kept and ignored triangle.

For a set P of points in the (d-dimensional) Euclidean space, a Delaunay triangulation is a triangulation DT(P) such that no point in P is inside the circum-hypersphere of any d-simplex in DT(P). It is known[1] that there exists a unique Delaunay triangulation for P if P is a set of points in general position; that is, the affine hull of P is d-dimensional and no set of d + 2 points in P lie on the boundary of a ball whose interior does not intersect P.

The problem of finding the Delaunay triangulation of a set of points in d-dimensional Euclidean space can be converted to the problem of finding the convex hull of a set of points in (d + 1)-dimensional space. This may be done by giving each point p an extra coordinate equal to |p|², thus turning it into a hyper-paraboloid (this is termed "lifting"); taking the bottom side of the convex hull (as the top end-cap faces upwards away from the origin, and must be discarded); and mapping back to d-dimensional space by deleting the last coordinate. As the convex hull is unique, so is the triangulation, assuming all facets of the convex hull are simplices. Nonsimplicial facets only occur when d + 2 of the original points lie on the same d-hypersphere, i.e., the points are not in general position. [2]

Example steps

Each frame of the animation shows a Delaunay triangulation of the four points. Halfway through, the triangulating edge flips showing that the Delaunay triangulation maximizes the minimum angle, not the edge-length of the triangles.

Let n be the number of points and d the number of dimensions.

• The union of all simplices in the triangulation is the convex hull of the points.
• The Delaunay triangulation contains O(n^{⌈d / 2⌉}) simplices.[3]
• In the plane (d = 2), if there are b vertices on the convex hull, then any triangulation of the points has at most 2n − 2 − b triangles, plus one exterior face (see Euler characteristic).
• If points are distributed according to a Poisson process in the plane with constant intensity, then each vertex has on average six surrounding triangles. More generally for the same process in d dimensions the average number of neighbors is a constant depending only on d.[4]
• In the plane, the Delaunay triangulation maximizes the minimum angle. Compared to any other triangulation of the points, the smallest angle in the Delaunay triangulation is at least as large as the smallest angle in any other. However, the Delaunay triangulation does not necessarily minimize the maximum angle.[5] The Delaunay triangulation also does not necessarily minimize the length of the edges.
• A circle circumscribing any Delaunay triangle does not contain any other input points in its interior.
• If a circle passing through two of the input points doesn't contain any other input points in its interior, then the segment connecting the two points is an edge of a Delaunay triangulation of the given points.
• Each triangle of the Delaunay triangulation of a set of points in d-dimensional spaces corresponds to a facet of convex hull of the projection of the points onto a (d + 1)-dimensional paraboloid, and vice versa.
• The closest neighbor b to any point p is on an edge bp in the Delaunay triangulation since the nearest neighbor graph is a subgraph of the Delaunay triangulation.
• The Delaunay triangulation is a geometric spanner: In the plane (d = 2), the shortest path between two vertices, along Delaunay edges, is known to be no longer than 1.998 times the Euclidean distance between them.[6]

From the above properties an important feature arises: Looking at two triangles ABD and BCD with the common edge BD (see figures), if the sum of the angles α and γ is less than or equal to 180°, the triangles meet the Delaunay condition.

This is an important property because it allows the use of a flipping technique. If two triangles do not meet the Delaunay condition, switching the common edge BD for the common edge AC produces two triangles that do meet the Delaunay condition:

• 
  This triangulation does not meet the Delaunay condition (the sum of α and γ is bigger than 180°).
• 
  This pair of triangles does not meet the Delaunay condition (the circumcircle contains more than three points).
• 
  Flipping the common edge produces a valid Delaunay triangulation for the four points.

This operation is called a flip, and can be generalised to three and higher dimensions.[7]

We need a robust and fast way to detect if point D lies in the circumcircle of A, B, C

Many algorithms for computing Delaunay triangulations rely on fast operations for detecting when a point is within a triangle's circumcircle and an efficient data structure for storing triangles and edges. In two dimensions, one way to detect if point D lies in the circumcircle of A, B, C is to evaluate the determinant:[8]

${\displaystyle {\begin{aligned}&{\begin{vmatrix}A_{x}&A_{y}&A_{x}^{2}+A_{y}^{2}&1\\B_{x}&B_{y}&B_{x}^{2}+B_{y}^{2}&1\\C_{x}&C_{y}&C_{x}^{2}+C_{y}^{2}&1\\D_{x}&D_{y}&D_{x}^{2}+D_{y}^{2}&1\end{vmatrix}}={\begin{vmatrix}A_{x}-D_{x}&A_{y}-D_{y}&(A_{x}^{2}-D_{x}^{2})+(A_{y}^{2}-D_{y}^{2})\\B_{x}-D_{x}&B_{y}-D_{y}&(B_{x}^{2}-D_{x}^{2})+(B_{y}^{2}-D_{y}^{2})\\C_{x}-D_{x}&C_{y}-D_{y}&(C_{x}^{2}-D_{x}^{2})+(C_{y}^{2}-D_{y}^{2})\end{vmatrix}}\\[8pt]={}&{\begin{vmatrix}A_{x}-D_{x}&A_{y}-D_{y}&(A_{x}-D_{x})^{2}+(A_{y}-D_{y})^{2}\\B_{x}-D_{x}&B_{y}-D_{y}&(B_{x}-D_{x})^{2}+(B_{y}-D_{y})^{2}\\C_{x}-D_{x}&C_{y}-D_{y}&(C_{x}-D_{x})^{2}+(C_{y}-D_{y})^{2}\end{vmatrix}}>0\end{aligned}}}$

When A, B and C are sorted in a counterclockwise order, this determinant is positive if and only if D lies inside the circumcircle.

The Euclidean minimum spanning tree of a set of points is a subset of the Delaunay triangulation of the same points,[20] and this can be exploited to compute it efficiently.

For modelling terrain or other objects given a set of sample points, the Delaunay triangulation gives a nice set of triangles to use as polygons in the model. In particular, the Delaunay triangulation avoids narrow triangles (as they have large circumcircles compared to their area). See triangulated irregular network.

Delaunay triangulations can be used to determine the density or intensity of points samplings by means of the Delaunay tessellation field estimator (DTFE).

A Delaunay triangulation of a random set of 100 points in a plane.

Delaunay triangulations are often used to generate meshes for space-discretised solvers such as the finite element method and the finite volume method of physics simulation, because of the angle guarantee and because fast triangulation algorithms have been developed. Typically, the domain to be meshed is specified as a coarse simplicial complex; for the mesh to be numerically stable, it must be refined, for instance by using Ruppert's algorithm.

The increasing popularity of finite element method and boundary element method techniques increases the incentive to improve automatic meshing algorithms. However, all of these algorithms can create distorted and even unusable grid elements. Fortunately, several techniques exist which can take an existing mesh and improve its quality. For example, smoothing (also referred to as mesh refinement) is one such method, which repositions nodes to minimize element distortion. The stretched grid method allows the generation of pseudo-regular meshes that meet the Delaunay criteria easily and quickly in a one-step solution.

Constrained Delaunay triangulation has found applications in path planning in automated driving and topographic surveying. [21]

• Delaunay triangulation in CGAL, the Computational Geometry Algorithms Library:
  • Mariette Yvinec. 2D Triangulation. Retrieved April 2010.
  • Pion, Sylvain; Teillaud, Monique. 3D Triangulations. Retrieved April 2010.
  • Hornus, Samuel; Devillers, Olivier; Jamin, Clément. dD Triangulations.
  • Hert, Susan; Seel, Michael. dD Convex Hulls and Delaunay Triangulations. Retrieved April 2010.
• "Delaunay triangulation". Wolfram MathWorld. Retrieved April 2010.
• "Poly2Tri: Incremental constrained Delaunay triangulation. Open source C++ implementation. Retrieved April 2019.
• "Divide & Conquer Delaunay triangulation construction". Open source C99 implementation. Retrieved April 2019.