平面点集Delaunay三角剖分的分治算法

为发展图形网格化技术,研究了平面点集的三角剖分算法.根据经典算法中在实际应用中遇到的共性问题,提炼了3个工具算法;为了更好地表示平面区域划分的拓扑信息,引入了双链接边表(DCEL)的数据结构.在此基础上,设计并实现了平面集Delaunay三角剖分分治算法,并对特殊退化情况进行了处理,通过计算表明了该算法时间复杂度为0(N* logN).实验数据结果验证了该算法的正确性、健壮性.     

基于Delaunay三角剖分生成Voronoi图算法

针对Delaunay三角网生长算法和间接生成Voronoi图算法构网效率不高的问题,提出了一种Delaunay三角网生长法间接生成Voronoi图的改进算法。该算法以点集凸壳上一边快速生成种子三角形,定义了半封闭边界点的概念,在三角形扩展过程中动态删除封闭点及半封闭边界点,加快Delaunay三角网生成速度。然后又定义了有序目标三角形的概念,该算法能迅速查找点的有序目标三角形,生成无射线的Voronoi图;考虑凸壳上点的特性,借助三个无穷点生成带射线的Voronoi图。通过实验结果分析表明,改进的算法执行效率有了很大提高。     

三维点集Voronoi图的算法实现

对现有三维点集Voronoi图的生成算法进行深入研究,提出并实现由Delaunay三角剖分构建Voronoi图的算法．首先采用随机增量局部转换计算Delaunay三角剖分,然后再根据对偶特性构建Voronoi图．该算法健壮性很高,适用于处理各种非完全共面三维点集．     

Point placement algorithms for Delaunay triangulation of polygonal domains

In some applications of triangulation, such as finite-element mesh generation, the aim is to triangulate a given domain, not just a set of points. One approach to meeting this requirement, while maintaining the desirable properties of Delaunay triangulation, has been to enforce the empty circumcircle property of Delaunay triangulation, subject to the additional constraint that the edges of a polygon be covered by edges of the triangulation. In finite-element mesh generation it is usually necessary to include additional points besides the vertices of the domain boundary. This motivates us to ask whether it is possible to trinagulate a domain by introducing additional points in such a manner that the Delaunay triangulation of the points includes the edges of the domain boundary. We present algorithms that given a multiply connected polygonal domain withN vertices, placeK additional points on the boundary inO(N logN + K) time such that the polygon is covered by the edges of the Delaunay triangulation of theN + K points. Furthermore,K is the minimum number of additional points such that a circle, passing through the endpoints of each boundary edge segment, exists that does not contain in its interior any other part of the domain boundary. We also show that by adding only one more point per edge, certain degeneracies that may otherwise arise can be avoided.     

Splitting a Delaunay Triangulation in Linear Time

Abstract. Computing the Delaunay triangulation of n points requires usually a minimum of Ω(n log n) operations, but in some special cases where some additional knowledge is provided, faster algorithms can be designed. Given two sets of points, we prove that, if the Delaunay triangulation of all the points is known, the Delaunay triangulation of each set can be computed in randomized expected linear time.     

An efficient sweep-line Delaunay triangulation algorithm

This paper introduces a new algorithm for constructing a 2D Delaunay triangulation. It is based on a sweep-line paradigm, which is combined with a local optimization criterion—a characteristic of incremental insertion algorithms. The sweep-line status is represented by a so-called advancing front, which is implemented as a hash-table. Heuristics have been introduced to prevent the construction of tiny triangles, which would probably be legalized. This algorithm has been compared with other popular Delaunay algorithms and it is the fastest algorithm among them. In addition, this algorithm does not use a lot of memory for supporting data structure, it is easy to understand and simple to implement.     

以优先点为中心的Delaunay三角网生长算法

目的 Delaunay三角网具备的优良性质使其得到广泛的应用,构建Delaunay三角网是计算几何的基础问题之一,为了高效、准确地构建大规模点集的Delaunay三角网,提出一种基于优先点的改进三角网生长算法.方法 算法以逆时针次序的一条凸包边为初始基边,使用基边对角最大化并按照逆时针次序选定第3点构建一个Delaunay三角形,通过待扩展边列表中的数据判断新生成的两条边是否需要扩展,采用先进先出的方式从待扩展边列表中取边作为基边,以优先点为中心构建局部Delaunay三角网使优先点尽快成为封闭点,再从点集中删除此封闭点.结果 对于同一测试点集,改进算法运行时间与经典算法运行时间的比率不超过1/3,且此比率随点集规模增长逐步下降.相比经典算法,改进算法在时间效率上有较大提升.结论 本文改进算法对点集规模具有较好的自适应性与较高的构网效率,可用于大规模场景下Delaunay三角网的构建.     

Computing multidimensional Delaunay tessellations

We recall some properties of Voronoi and Delaunay tessellations in any numbers of dimensions. We then propose a solution to the following problem: Given the Delaunay tessellation of n d-dimensional data points X₁,…, X_n, the proble is to insert a new data point X and to update the tessellation accordingly. The solution proposed achieves minimum space-complexity.     

Insert and delete algorithms for maintaining dynamic Delaunay triangulations

We recall that optimal condensing of nearest neighbor data requires the construction of the Delaunay triangulation of the training set. We argue that, from the viewpoint of computational complexity, an iterative approach using a dynamic triangulation is most desirable. We describe two algorithms, Insert and Delete, which permit to maintain a dynamic Delaunay triangulation.