Delaunay triangulation

Last updated April 16, 2024

In computational geometry, a Delaunay triangulation or Delone triangulation of a set of points in the plane subdivides their convex hull ^[1] 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.

If the points all lie on a straight line, the notion of triangulation becomes degenerate and there is no Delaunay triangulation. For four or more points on the same circle (e.g., the vertices of a rectangle) the Delaunay triangulation is not unique: each of the two possible triangulations that split the quadrangle into two triangles satisfies the "Delaunay condition", i.e., the requirement that the circumcircles of all triangles have empty interiors.

By considering circumscribed spheres, the notion of Delaunay triangulation extends to three and higher dimensions. Generalizations are possible to metrics other than Euclidean distance. However, in these cases a Delaunay triangulation is not guaranteed to exist or be unique.

Relationship with the Voronoi diagram

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.

d-dimensional Delaunay

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^[2] 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.^[3]

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 $\textstyle O{\bigl (}n^{\lceil d/2\rceil }{\bigr )}$ simplices.^[4]
In the plane ( $d = 2$ ), if there are $b$ vertices on the convex hull, then any triangulation of the points has at most $2 n - 2 - b$ 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 $d$ .^[5]
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.^[6] 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.^[7]

Visual Delaunay definition: Flipping

From the above properties an important feature arises: Looking at two triangles $△ ABD, △ BCD$ with the common edge $BD$ (see figures), if the sum of the angles $α + γ \leq 180°$ , 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 (there is a point within the interior of the circumcircle).
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.^[8]

Algorithms

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:^[9]

{\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}}\\[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 $A, B, C$ are sorted in a counterclockwise order, this determinant is positive only if $D$ lies inside the circumcircle.

Flip algorithms

As mentioned above, if a triangle is non-Delaunay, we can flip one of its edges. This leads to a straightforward algorithm: construct any triangulation of the points, and then flip edges until no triangle is non-Delaunay. Unfortunately, this can take $Ω(n 2)$ edge flips.^[10] While this algorithm can be generalised to three and higher dimensions, its convergence is not guaranteed in these cases, as it is conditioned to the connectedness of the underlying flip graph: this graph is connected for two-dimensional sets of points, but may be disconnected in higher dimensions.^[8]

Incremental

The most straightforward way of efficiently computing the Delaunay triangulation is to repeatedly add one vertex at a time, retriangulating the affected parts of the graph. When a vertex $v$ is added, we split in three the triangle that contains $v$ , then we apply the flip algorithm. Done naïvely, this will take $O(n)$ time: we search through all the triangles to find the one that contains $v$ , then we potentially flip away every triangle. Then the overall runtime is $O(n 2)$ .

If we insert vertices in random order, it turns out (by a somewhat intricate proof) that each insertion will flip, on average, only $O(1)$ triangles – although sometimes it will flip many more.^[11] This still leaves the point location time to improve. We can store the history of the splits and flips performed: each triangle stores a pointer to the two or three triangles that replaced it. To find the triangle that contains $v$ , we start at a root triangle, and follow the pointer that points to a triangle that contains $v$ , until we find a triangle that has not yet been replaced. On average, this will also take $O(log n)$ time. Over all vertices, then, this takes $O(n log n)$ time.^[12] While the technique extends to higher dimension (as proved by Edelsbrunner and Shah^[13]), the runtime can be exponential in the dimension even if the final Delaunay triangulation is small.

The Bowyer–Watson algorithm provides another approach for incremental construction. It gives an alternative to edge flipping for computing the Delaunay triangles containing a newly inserted vertex.

Unfortunately the flipping-based algorithms are generally hard to parallelize, since adding some certain point (e.g. the center point of a wagon wheel) can lead to up to $O(n)$ consecutive flips. Blelloch et al.^[14] proposed another version of incremental algorithm based on rip-and-tent, which is practical and highly parallelized with polylogarithmic span.

Divide and conquer

A divide and conquer algorithm for triangulations in two dimensions was developed by Lee and Schachter and improved by Guibas and Stolfi ^[9]^[15] and later by Dwyer.^[16] 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 $O(n log n)$ .^[17]

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 and conquer Delaunay triangulation algorithm in E^d" by P. Cignoni, C. Montani, R. Scopigno.^[18]

The divide and conquer algorithm has been shown to be the fastest DT generation technique sequentially.^[19]^[20]

Sweephull

Sweephull^[21] is a hybrid technique for 2D Delaunay triangulation that uses a radially propagating sweep-hull, and a flipping algorithm. The sweep-hull is created sequentially by iterating a radially-sorted set of 2D points, and connecting triangles to the visible part of the convex hull, which gives a non-overlapping triangulation. One can build a convex hull in this manner so long as the order of points guarantees no point would fall within the triangle. But, radially sorting should minimize flipping by being highly Delaunay to start. This is then paired with a final iterative triangle flipping step.

Applications

The Euclidean minimum spanning tree of a set of points is a subset of the Delaunay triangulation of the same points,^[22] and this can be exploited to compute it efficiently.

For modelling terrain or other objects given a point cloud, 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).

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. ^[23]

Related Research Articles

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.

Computational geometry is a branch of computer science devoted to the study of algorithms which can be stated in terms of geometry. Some purely geometrical problems arise out of the study of computational geometric algorithms, and such problems are also considered to be part of computational geometry. While modern computational geometry is a recent development, it is one of the oldest fields of computing with a history stretching back to antiquity.

In mathematics, a Voronoi diagram is a partition of a plane into regions close to each of a given set of objects. It can be classified also as a tessellation. In the simplest case, these objects are just finitely many points in the plane. For each seed there is a corresponding region, called a Voronoi cell, consisting of all points of the plane closer to that seed than to any other. The Voronoi diagram of a set of points is dual to that set's Delaunay triangulation.

<span class="mw-page-title-main">Polygon triangulation</span> Partition of a simple polygon into triangles

In computational geometry, polygon triangulation is the partition of a polygonal area $P$ into a set of triangles, i.e., finding a set of triangles with pairwise non-intersecting interiors whose union is $P$ .

A Euclidean minimum spanning tree of a finite set of points in the Euclidean plane or higher-dimensional Euclidean space connects the points by a system of line segments with the points as endpoints, minimizing the total length of the segments. In it, any two points can reach each other along a path through the line segments. It can be found as the minimum spanning tree of a complete graph with the points as vertices and the Euclidean distances between points as edge weights.

In geometry, a simple polygon is a polygon that does not intersect itself and has no holes. That is, it is a piecewise-linear Jordan curve consisting of finitely many line segments. These polygons include as special cases the convex polygons, star-shaped polygons, and monotone polygons.

<span class="mw-page-title-main">Point-set triangulation</span> Simplicial complex in Euclidean geometry

A triangulation of a set of points $in the Euclidean space is a simplicial complex that covers the convex hull of, and whose vertices belong to . In the plane, triangulations are made up of triangles, together with their edges and vertices. Some authors require that all the points of are vertices of its triangulations. In this case, a triangulation of a set of points in the plane can alternatively be defined as a maximal set of non-crossing edges between points of . In the plane, triangulations are special cases of planar straight-line graphs.$

In geometry, a triangulation is a subdivision of a planar object into triangles, and by extension the subdivision of a higher-dimension geometric object into simplices. Triangulations of a three-dimensional volume would involve subdividing it into tetrahedra packed together.

In geometry, the circumscribed circle or circumcircle of a triangle is a circle that passes through all three vertices. The center of this circle is called the circumcenter of the triangle, and its radius is called the circumradius. The circumcenter is the point of intersection between the three perpendicular bisectors of the triangle's sides, and is a triangle center.

In the mathematical discipline of graph theory, the dual graph of a planar graph $G$ is a graph that has a vertex for each face of $G$ . The dual graph has an edge for each pair of faces in $G$ that are separated from each other by an edge, and a self-loop when the same face appears on both sides of an edge. Thus, each edge $e$ of $G$ has a corresponding dual edge, whose endpoints are the dual vertices corresponding to the faces on either side of $e$ . The definition of the dual depends on the choice of embedding of the graph $G$ , so it is a property of plane graphs rather than planar graphs. For planar graphs generally, there may be multiple dual graphs, depending on the choice of planar embedding of the graph.

<span class="mw-page-title-main">Lloyd's algorithm</span> Algorithm used for points in euclidean space

In electrical engineering and computer science, Lloyd's algorithm, also known as Voronoi iteration or relaxation, is an algorithm named after Stuart P. Lloyd for finding evenly spaced sets of points in subsets of Euclidean spaces and partitions of these subsets into well-shaped and uniformly sized convex cells. Like the closely related k-means clustering algorithm, it repeatedly finds the centroid of each set in the partition and then re-partitions the input according to which of these centroids is closest. In this setting, the mean operation is an integral over a region of space, and the nearest centroid operation results in Voronoi diagrams.

<span class="mw-page-title-main">Triangulated irregular network</span> Representation of a surface as a triangle mesh with elevated vertices

In computer graphics, a triangulated irregular network (TIN) is a representation of a continuous surface consisting entirely of triangular facets, used mainly as Discrete Global Grid in primary elevation modeling.

Mesh generation is the practice of creating a mesh, a subdivision of a continuous geometric space into discrete geometric and topological cells. Often these cells form a simplicial complex. Usually the cells partition the geometric input domain. Mesh cells are used as discrete local approximations of the larger domain. Meshes are created by computer algorithms, often with human guidance through a GUI, depending on the complexity of the domain and the type of mesh desired. A typical goal is to create a mesh that accurately captures the input domain geometry, with high-quality (well-shaped) cells, and without so many cells as to make subsequent calculations intractable. The mesh should also be fine in areas that are important for the subsequent calculations.

<span class="mw-page-title-main">Pseudotriangle</span>

In Euclidean plane geometry, a pseudotriangle (pseudo-triangle) is the simply connected subset of the plane that lies between any three mutually tangent convex sets. A pseudotriangulation (pseudo-triangulations) is a partition of a region of the plane into pseudotriangles, and a pointed pseudotriangulation is a pseudotriangulation in which at each vertex the incident edges span an angle of less than π.

<span class="mw-page-title-main">Pitteway triangulation</span>

In computational geometry, a Pitteway triangulation is a point set triangulation in which the nearest neighbor of any point p within the triangulation is one of the vertices of the triangle containing p. Alternatively, it is a Delaunay triangulation in which each internal edge crosses its dual Voronoi diagram edge. Pitteway triangulations are named after Michael Pitteway, who studied them in 1973. Not every point set supports a Pitteway triangulation. When such a triangulation exists it is a special case of the Delaunay triangulation, and consists of the union of the Gabriel graph and convex hull.

In computational geometry, the Bowyer–Watson algorithm is a method for computing the Delaunay triangulation of a finite set of points in any number of dimensions. The algorithm can be also used to obtain a Voronoi diagram of the points, which is the dual graph of the Delaunay triangulation.

Algorithms that construct convex hulls of various objects have a broad range of applications in mathematics and computer science.

In mesh generation, Delaunay refinements are algorithms for mesh generation based on the principle of adding Steiner points to the geometry of an input to be meshed, in a way that causes the Delaunay triangulation or constrained Delaunay triangulation of the augmented input to meet the quality requirements of the meshing application. Delaunay refinement methods include methods by Chew and by Ruppert.

In computational geometry, a constrained Delaunay triangulation is a generalization of the Delaunay triangulation that forces certain required segments into the triangulation as edges, unlike the Delaunay triangulation itself which is based purely on the position of a given set of vertices without regard to how they should be connected by edges. It can be computed efficiently and has applications in geographic information systems and in mesh generation.

<span class="mw-page-title-main">Flip graph</span> A graph that encodes local operations in mathematics

In mathematics, a flip graph is a graph whose vertices are combinatorial or geometric objects, and whose edges link two of these objects when they can be obtained from one another by an elementary operation called a flip. Flip graphs are special cases of geometric graphs.

References

↑ Loosely speaking, the region that a rubber band stretched around the points would enclose.
1 2 Delaunay, Boris (1934). "Sur la sphère vide" [On the empty sphere]. Bulletin de l'Académie des Sciences de l'URSS, Classe des Sciences Mathématiques et Naturelles (in French). 6: 793–800.
↑ Fukuda, Komei. "Frequently Asked Questions in Polyhedral Computation". www.cs.mcgill.ca. Retrieved 29 October 2018.
↑ Seidel, Raimund (1995). "The upper bound theorem for polytopes: an easy proof of its asymptotic version". Computational Geometry . 5 (2): 115–116. doi:10.1016/0925-7721(95)00013-Y.
↑ Meijering, J. L. (1953). "Interface area, edge length, and number of vertices in crystal aggregates with random nucleation" (PDF). Philips Research Reports. 8: 270–290. Archived from the original (PDF) on 2017-03-08. As cited by Dwyer, Rex A. (1991). "Higher-dimensional Voronoĭ diagrams in linear expected time". Discrete and Computational Geometry . 6 (4): 343–367. doi: 10.1007/BF02574694 . MR 1098813.
↑ Edelsbrunner, Herbert; Tan, Tiow Seng; Waupotitsch, Roman (1992). "An O(n² log n) time algorithm for the minmax angle triangulation" (PDF). SIAM Journal on Scientific and Statistical Computing. 13 (4): 994–1008. CiteSeerX 10.1.1.66.2895 . doi:10.1137/0913058. MR 1166172. Archived from the original (PDF) on 2017-02-09. Retrieved 2017-10-24..
↑ Xia, Ge (2013). "The stretch factor of the Delaunay triangulation is less than 1.998". SIAM Journal on Computing . 42 (4): 1620–1659. arXiv: 1103.4361 . doi:10.1137/110832458. MR 3082502. S2CID 6646528.
1 2 De Loera, Jesús A.; Rambau, Jörg; Santos, Francisco (2010). Triangulations, Structures for Algorithms and Applications. Algorithms and Computation in Mathematics. Vol. 25. Springer.
1 2 Guibas, Leonidas; Stolfi, Jorge (1985). "Primitives for the manipulation of general subdivisions and the computation of Voronoi". ACM Transactions on Graphics . 4 (2): 74–123. doi: 10.1145/282918.282923 . S2CID 52852815.
↑ Hurtado, F.; Noy, M.; Urrutia, J. (1999). "Flipping Edges in Triangulations". Discrete & Computational Geometry . 22 (3): 333–346. doi: 10.1007/PL00009464 .
↑ Guibas, Leonidas J.; Knuth, Donald E.; Sharir, Micha (1992). "Randomized incremental construction of Delaunay and Voronoi diagrams". Algorithmica . 7 (1–6): 381–413. doi:10.1007/BF01758770. S2CID 3770886.
↑ de Berg, Mark; Otfried Cheong; Marc van Kreveld; Mark Overmars (2008). Computational Geometry: Algorithms and Applications (PDF). Springer-Verlag. ISBN 978-3-540-77973-5. Archived from the original (PDF) on 2009-10-28. Retrieved 2010-02-23.
↑ Edelsbrunner, Herbert; Shah, Nimish (1996). "Incremental Topological Flipping Works for Regular Triangulations". Algorithmica . 15 (3): 223–241. doi:10.1007/BF01975867. S2CID 12976796.
↑ Blelloch, Guy; Gu, Yan; Shun, Julian; and Sun, Yihan. Parallelism in Randomized Incremental Algorithms Archived 2018-04-25 at the Wayback Machine . SPAA 2016. doi:10.1145/2935764.2935766.
↑ Peterson, Samuel. "COMPUTING CONSTRAINED DELAUNAY TRIANGULATIONS IN THE PLANE". www.geom.uiuc.edu. Archived from the original on 22 September 2017. Retrieved 25 April 2018.
↑ Dwyer, Rex A. (November 1987). "A faster divide-and-conquer algorithm for constructing delaunay triangulations". Algorithmica. 2 (1–4): 137–151. doi:10.1007/BF01840356. S2CID 10828441.
↑ Leach, G. (June 1992). "Improving Worst-Case Optimal Delaunay Triangulation Algorithms". 4th Canadian Conference on Computational Geometry. CiteSeerX 10.1.1.56.2323 .
↑ Cignoni, P.; C. Montani; R. Scopigno (1998). "DeWall: A fast divide and conquer Delaunay triangulation algorithm in E^d". Computer-Aided Design. 30 (5): 333–341. doi:10.1016/S0010-4485(97)00082-1.
↑ A Comparison of Sequential Delaunay Triangulation Algorithms "Archived copy" (PDF). Archived from the original (PDF) on 2012-03-08. Retrieved 2010-08-18.{{cite web}}: CS1 maint: archived copy as title (link)
↑ "Triangulation Algorithms and Data Structures". www.cs.cmu.edu. Archived from the original on 10 October 2017. Retrieved 25 April 2018.
↑ "S-hull" (PDF). s-hull.org. Archived (PDF) from the original on 2013-10-27. Retrieved 25 April 2018.
↑ Franz Aurenhammer; Rolf Klein; Der-tsai Lee (26 June 2013). Voronoi Diagrams And Delaunay Triangulations. World Scientific Publishing Company. pp. 197–. ISBN 978-981-4447-65-2.
↑ Sterling J Anderson; Sisir B. Karumanchi; Karl Iagnemma (5 July 2012). "Constraint-based planning and control for safe, semi-autonomous operation of vehicles" (PDF). 2012 IEEE Intelligent Vehicles Symposium. IEEE. doi:10.1109/IVS.2012.6232153. Archived from the original (PDF) on 28 February 2019. Retrieved 27 February 2019.

External links

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.
"Poly2Tri: Incremental constrained Delaunay triangulation. Open source C++ implementation. Retrieved April 2019.
"Divide & Conquer Delaunay triangulation construction". Open source C99 implementation. Retrieved April 2019.
"CDT: Constrained Delaunay Triangulation in C++". Open source C++ implementation. Retrieved August 2022.

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[1] Loosely speaking, the region that a rubber band stretched around the points would enclose.

[Delaunay1934-2] 1 2 Delaunay, Boris (1934). "Sur la sphère vide" [On the empty sphere]. Bulletin de l'Académie des Sciences de l'URSS, Classe des Sciences Mathématiques et Naturelles (in French). 6: 793–800.

[Fukuda-3] Fukuda, Komei. "Frequently Asked Questions in Polyhedral Computation". www.cs.mcgill.ca. Retrieved 29 October 2018.

[Seidel1995-4] Seidel, Raimund (1995). "The upper bound theorem for polytopes: an easy proof of its asymptotic version". Computational Geometry . 5 (2): 115–116. doi:10.1016/0925-7721(95)00013-Y.

[Meijering-5] Meijering, J. L. (1953). "Interface area, edge length, and number of vertices in crystal aggregates with random nucleation" (PDF). Philips Research Reports. 8: 270–290. Archived from the original (PDF) on 2017-03-08. As cited by Dwyer, Rex A. (1991). "Higher-dimensional Voronoĭ diagrams in linear expected time". Discrete and Computational Geometry . 6 (4): 343–367. doi: 10.1007/BF02574694 . MR 1098813.

[ETW1992-6] Edelsbrunner, Herbert; Tan, Tiow Seng; Waupotitsch, Roman (1992). "An O(n² log n) time algorithm for the minmax angle triangulation" (PDF). SIAM Journal on Scientific and Statistical Computing. 13 (4): 994–1008. CiteSeerX 10.1.1.66.2895 . doi:10.1137/0913058. MR 1166172. Archived from the original (PDF) on 2017-02-09. Retrieved 2017-10-24..

[Xia-7] Xia, Ge (2013). "The stretch factor of the Delaunay triangulation is less than 1.998". SIAM Journal on Computing . 42 (4): 1620–1659. arXiv: 1103.4361 . doi:10.1137/110832458. MR 3082502. S2CID 6646528.

[DRS-8] 1 2 De Loera, Jesús A.; Rambau, Jörg; Santos, Francisco (2010). Triangulations, Structures for Algorithms and Applications. Algorithms and Computation in Mathematics. Vol. 25. Springer.

[GS1985-9] 1 2 Guibas, Leonidas; Stolfi, Jorge (1985). "Primitives for the manipulation of general subdivisions and the computation of Voronoi". ACM Transactions on Graphics . 4 (2): 74–123. doi: 10.1145/282918.282923 . S2CID 52852815.

[Hurtado-10] Hurtado, F.; Noy, M.; Urrutia, J. (1999). "Flipping Edges in Triangulations". Discrete & Computational Geometry . 22 (3): 333–346. doi: 10.1007/PL00009464 .

[GKS1992-11] Guibas, Leonidas J.; Knuth, Donald E.; Sharir, Micha (1992). "Randomized incremental construction of Delaunay and Voronoi diagrams". Algorithmica . 7 (1–6): 381–413. doi:10.1007/BF01758770. S2CID 3770886.

[deBerg-12] Berg, Mark; Otfried Cheong; Marc van Kreveld; Mark Overmars (2008). Computational Geometry: Algorithms and Applications (PDF). Springer-Verlag. ISBN 978-3-540-77973-5. Archived from the original (PDF) on 2009-10-28. Retrieved 2010-02-23.

[ES1996-13] Edelsbrunner, Herbert; Shah, Nimish (1996). "Incremental Topological Flipping Works for Regular Triangulations". Algorithmica . 15 (3): 223–241. doi:10.1007/BF01975867. S2CID 12976796.

[Parallel-14] Blelloch, Guy; Gu, Yan; Shun, Julian; and Sun, Yihan. Parallelism in Randomized Incremental Algorithms Archived 2018-04-25 at the Wayback Machine . SPAA 2016. doi:10.1145/2935764.2935766.

[Peterson-15] Peterson, Samuel. "COMPUTING CONSTRAINED DELAUNAY TRIANGULATIONS IN THE PLANE". www.geom.uiuc.edu. Archived from the original on 22 September 2017. Retrieved 25 April 2018.

[Dwyer1987-16] Dwyer, Rex A. (November 1987). "A faster divide-and-conquer algorithm for constructing delaunay triangulations". Algorithmica. 2 (1–4): 137–151. doi:10.1007/BF01840356. S2CID 10828441.

[Leach1992-17] Leach, G. (June 1992). "Improving Worst-Case Optimal Delaunay Triangulation Algorithms". 4th Canadian Conference on Computational Geometry. CiteSeerX 10.1.1.56.2323 .

[CMS1998-18] Cignoni, P.; C. Montani; R. Scopigno (1998). "DeWall: A fast divide and conquer Delaunay triangulation algorithm in E^d". Computer-Aided Design. 30 (5): 333–341. doi:10.1016/S0010-4485(97)00082-1.

[19] A Comparison of Sequential Delaunay Triangulation Algorithms "Archived copy" (PDF). Archived from the original (PDF) on 2012-03-08. Retrieved 2010-08-18.{{cite web}}: CS1 maint: archived copy as title (link)

[20] "Triangulation Algorithms and Data Structures". www.cs.cmu.edu. Archived from the original on 10 October 2017. Retrieved 25 April 2018.

[21] "S-hull" (PDF). s-hull.org. Archived (PDF) from the original on 2013-10-27. Retrieved 25 April 2018.

[AK2013-22] Franz Aurenhammer; Rolf Klein; Der-tsai Lee (26 June 2013). Voronoi Diagrams And Delaunay Triangulations. World Scientific Publishing Company. pp. 197–. ISBN 978-981-4447-65-2.

[AKI2012-23] Sterling J Anderson; Sisir B. Karumanchi; Karl Iagnemma (5 July 2012). "Constraint-based planning and control for safe, semi-autonomous operation of vehicles" (PDF). 2012 IEEE Intelligent Vehicles Symposium. IEEE. doi:10.1109/IVS.2012.6232153. Archived from the original (PDF) on 28 February 2019. Retrieved 27 February 2019.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]