Delaunay triangulation

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In mathematics, and computational geometry, a Delaunay triangulation or Delone triangularization for a set P of points in the plane is a triangulation DT(P) such that no point in P is inside the circumcircle of any triangle in DT(P). Delaunay triangulations maximize the minimum angle of all the angles of the triangles in the triangulation; they tend to avoid "sliver" triangles. The triangulation was invented by Boris Delaunay in 1934 [1].

This is the Delaunay triangulation of a random set of points in the plane.

In the general n-dimensional case it is stated as follows: For a set P of points in the (n-dimensional) Euclidean space, a Delaunay triangulation is a triangulation DT(P) such that no point in P is inside the circum-hypersphere of any simplex in DT(P).

It is known that there exists a unique Delaunay triangulation for P, if P is a set of points in general position; that is, no three points are on the same line and no four are on the same circle, for a two dimensional set of points, or no n + 1 points are on the same hyperplane and no n + 2 points are on the same hypersphere, for an n-dimensional set of points. An elegant proof of this fact is outlined below. It is worth mentioning, because it reveals connections between the two constructs fundamental for computational and combinatorial geometry.

The Delaunay triangulation of a discrete point set P corresponds to the dual graph of the Voronoi tessellation for P.

The problem of finding the Delaunay triangulation of a set of points in n-dimensional Euclidean space can be converted to the problem of finding the convex hull of a set of points in (n + 1)-dimensional space, by giving each point $p$ an extra coordinate equal to $| p | 2$ , taking the bottom side of the convex hull, and mapping back to n-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. A facet not being a simplex implies that n + 2 of the original points lay on the same d-hypersphere, and the points were not in general position.

On the other hand, it is easily seen that for the set of three points on the same line there is no Delaunay triangulation (in fact, no triangulation at all). On the other hand, for 4 points on the same circle (e.g., the vertices of a rectangle) the Delaunay triangulation is not unique: clearly, the two possible triangulations that split the quadrangle into two triangles satisfy the Delaunay condition.

Generalizations are possible to metrics other than Euclidean. However in these cases a Delaunay triangulation is not guaranteed to exist or be unique.

1 Properties
2 Algorithms
- 2.1 Incremental
- 2.2 Divide and conquer
3 Applications
4 See also
5 References
6 External links

[edit] Properties

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 at most $O(n^{\lceil d/2 \rceil})$ simplices.
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).
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. The converse is not true: the Delaunay triangulation does not necessarily minimize the maximum angle.

[edit] Algorithms

All 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

$\begin{vmatrix}A_x&A_y&A_x^2 + A_y^2&1\\ B_x&B_y&B_x^2 + B_y^2&1\\ C_x&C_y&C_x^2 + C_y^2&1\\ D_x&D_y&D_x^2 + D_y^2&1\end{vmatrix}$

Assuming A, B and C to lie counter-clockwise, this is positive if and only if D lies in the circumcircle.

[edit] Incremental

The most straightforward way of computing the Delaunay triangulation is to repeatedly add one vertex at a time, retriangulating the affected parts of the graph. When a vertex is added, a search is done for all triangles' circumcircles containing the vertex. Then, those triangles are removed and that part of the graph retriangulated. Done naively, this results in a running time of O(n²).

A common way to speed up this method is sweepline, which involves sorting the vertices by one coordinate and adding them in that order. Then, one only needs to keep track of circumcircles containing points of large enough first coordinate. The expected running time in two dimensions in this case is O(n^3/2) although the worst case continues to be O(n²). Its performance at low numbers of points is competitive with divide and conquer (below) but does not scale as nicely above the tens of thousands of points. ^[1]

If one inserts the vertices in a random order, the expected running time is O(n log n).

Another efficient (O(n log n)) incremental algorithm keeps the whole history of the triangulation in the form of a tree. The elements replacing a conflicting element in an insertion are called its children. When a parent is in conflict with a point to be inserted, so are its children. This provides a fast way of getting the list of triangles to remove (which is the slowest part of any incremental insertion algorithm).

[edit] Divide and conquer

A divide and conquer algorithm for triangulations in two dimensions is due to Lee and Schachter which was improved by Guibas and Stolfi and later by Dwyer. In this algorithm, one recursively draws a line to split the vertices into two sets. The Delaunay triangulation is computed for each set, and then the two sets are merged along the splitting line. Using some clever tricks, the merge operation can be done in time O(n), so the total running time is O(n log n).

For certain types of point sets, such as a uniform random distribution, by intelligently picking the splitting lines the expected time can be reduced to O(n log log n) while still maintaining worst-case performance.

A divide and conquer paradigm to performing a triangulation in d-dimensions is presented in "DeWall: A Fast Divide & Conquer Delaunay Triangulation Algorithm in E^d" by P. Cignoni, C. Montani, R. Scopigno.

[edit] Applications

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

For modeling 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).

Delaunay triangulations are often used to build meshes for the finite element method, because of the angle guarantee and the fact that we know fast triangulation algorithms. 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. This has been implemented by Jonathan Shewchuk in the freely available (yet non-free) Triangle package.