Algorithm for constrained delaunay triangulation

A direct algorithm for computing constrained Delaunay triangulation in 2-D is presented. The algorithm inserts points along the constrained edges (break lines) to maintain the Delaunay criterion. Since many different insertions are possible, the algorithm computes only those that are on the Delaunay circles of each intersected triangle. A shelling procedure is applied to put triangles together in such a way that completeness and correctness are guaranteed.     

An adaptive spatial clustering algorithm based on delaunay triangulation

In this paper, an adaptive spatial clustering algorithm based on Delaunay triangulation (ASCDT for short) is proposed. The ASCDT algorithm employs both statistical features of the edges of Delaunay triangulation and a novel spatial proximity definition based upon Delaunay triangulation to detect spatial clusters. Normally, this algorithm can automatically discover clusters of complicated shapes, and non-homogeneous densities in a spatial database, without the need to set parameters or prior knowledge. The user can also modify the parameter to fit with special applications. In addition, the algorithm is robust to noise. Experiments on both simulated and real-world spatial databases (i.e. an earthquake dataset in China) are utilized to demonstrate the effectiveness and advantages of the ASCDT algorithm.     

Constrained delaunay triangulations

Given a set ofn vertices in the plane together with a set of noncrossing, straight-line edges, theconstrained Delaunay triangulation (CDT) is the triangulation of the vertices with the following properties: (1) the prespecified edges are included in the triangulation, and (2) it is as close as possible to the Delaunay triangulation. We show that the CDT can be built in optimalO(n logn) time using a divide-and-conquer technique. This matches the time required to build an arbitrary (unconstrained) Delaunay triangulation and the time required to build an arbitrary constrained (non-Delaunay) triagulation. CDTs, because of their relationship with Delaunay triangulations, have a number of properties that make them useful for the finite-element method. Applications also include motion planning in the presence of polygonal obstacles and constrained Euclidean minimum spanning trees, spanning trees subject to the restriction that some edges are prespecified.An earlier version of the results presented here appeared in theProceedings of the Third Annual Symposium on Computational Geometry (1987).     

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。     

Two algorithms for constructing a Delaunay triangulation

This paper provides a unified discussion of the Delaunay triangulation. Its geometric properties are reviewed and several applications are discussed. Two algorithms are presented for constructing the triangulation over a planar set ofN points. The first algorithm uses a divide-and-conquer approach. It runs inO(N logN) time, which is asymptotically optimal. The second algorithm is iterative and requiresO(N ²) time in the worst case. However, its average case performance is comparable to that of the first algorithm.This work was supported in part by the National Science Foundation under grant MCS-76-17321 and the Joint Services Electronics Program under contract DAAB-07-72-0259.     

Trash removal algorithm for fast construction of the elliptic Gabriel graph using Delaunay triangulation

Given a set of points P, finding near neighbors among the points is an important problem in many applications in CAD/CAM, computer graphics, computational geometry, etc. In this paper, we propose an efficient algorithm for constructing the elliptic Gabriel graph (EGG), which is a generalization of the well-known Gabriel graph and parameterized by a non-negative value α. Our algorithm is based on the observation that a candidate point which may define an edge of an EGG with a given point p∈P is always in the scaled Voronoi region of p with a scale factor 2/α² when the parameter α<1. From this observation, we can reduce the search space for locating neighbors of p in the EGG. For the case of α≥1, due to the fact that EGG is a subgraph of the Delaunay graph of P, EGG can be efficiently computed by watching the validity of each edge in the Delaunay graph. The proposed algorithm is shown to have its time complexity as O(n³) in the worst case and O(n) in the average case when α is moderately close to unity. The idea presented in this paper may similarly apply to other problems for the proximity search for point sets.     

A faster divide-and-conquer algorithm for constructing delaunay triangulations

An easily implemented modification to the divide-and-conquer algorithm for computing the Delaunay triangulation ofn sites in the plane is presented. The change reduces its (n logn) expected running time toO(n log logn) for a large class of distributions that includes the uniform distribution in the unit square. Experimental evidence presented demonstrates that the modified algorithm performs very well forn2¹⁶, the range of the experiments. It is conjectured that the average number of edges it creates—a good measure of its efficiency—is no more than twice optimal forn less than seven trillion. The improvement is shown to extend to the computation of the Delaunay triangulation in theL _p metric for 1<p.This research was supported by National Science Foundation Grants DCR-8352081 and DCR-8416190.     

基于快速Delaunay三角化的散乱点曲面重建算法

针对现有三维重建算法速度较慢的问题,提出了一种基于快速Delaunay三角化的散乱数据点的三维重建算法。首先,提出一种新的平面Delaunay三角化插入点目标三角形定位算法,利用插入点的方向搜索线与三角形是否相交以及交点个数加速目标三角形定位,不用额外判断点是否在三角形内;其次,自动检测曲面漏洞,利用凸壳的边界拼接方法进行漏洞弥补。实验结果表明,本算法不仅能较好地重建出三维模型,而且有较高的效率。     

ParaStream: A parallel streaming Delaunay triangulation algorithm for LiDAR points on multicore architectures

This paper presents a robust parallel Delaunay triangulation algorithm called ParaStream for processing billions of points from nonoverlapped block LiDAR files. The algorithm targets ubiquitous multicore architectures. ParaStream integrates streaming computation with a traditional divide-and-conquer scheme, in which additional erase steps are implemented to reduce the runtime memory footprint. Furthermore, a kd-tree-based dynamic schedule strategy is also proposed to distribute triangulation and merging work onto the processor cores for improved load balance. ParaStream exploits most of the computing power of multicore platforms through parallel computing, demonstrating qualities of high data throughput as well as a low memory footprint. Experiments on a 2-Way-Quad-Core Intel Xeon platform show that ParaStream can triangulate approximately one billion LiDAR points (16.4 GB) in about 16 min with only 600 MB physical memory. The total speedup (including I/O time) is about 6.62 with 8 concurrent threads.     

高阶Delaunay三角网及生成算法研究

Delaunay三角剖分是构建数字地形模型的有效方法,但是该方法会引起人工大坝和局部极值问题,使得地形模型不能很好地反映原始地形的真实面貌。在Delaunay三角网的基础上提出了一种高阶Delaunay三角网,并给出了高阶Delaunay三角网生成算法。实验结果表明,高阶Delaunay三角网能够有效地减少地形中局部极小的数量,因此,采用高阶Delaunay三角网建立的地形模型更接近于实际地形。     

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.     

Delaunay三角剖分在离群点检测中的应用

在传统的基于[K]近邻的算法中,需要为算法设置邻居参数[k]的值,只有具备相关的先验知识才能确定合适的参数值。为了减少参数对于离群点检测的影响,提出了一种无需参数的基于Delaunay三角剖分的离群点检测算法。Delaunay三角剖分是数值分析以及图形学中的重要基础理论,它的构建无需任何参数,在三角剖分图中的每个数据对象与它空间上相邻的点都存在边直接相连,因此可以形成一种有效的邻居关系。算法首先通过Delaunay三角剖分形成每个点的空间邻居集合,然后根据每个点与它们空间邻居之间的分布特征,计算它们的离群程度,根据离群程度的大小判断该点是否为离群点。通过实验与相关的算法比较,算法具有更好的效果。     

增量构造Voronoi区域的改进算法

将Voronoi区域的半平面公共交集转换为Voronoi顶点与半平面的位置关系,提出一种简单的裁剪规则实现Voronoi区域的增量构造;该算法可以有效地处理半直线Voronoi边与直线Voronoi边以及节点共线等特殊情况。理论分析与实验结果表明,该增量构造Voronoi区域的平均时间复杂度是近似线性的。     

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.     

两维区域三角剖分的新算法

提出一种两维区域三角剖分的新算法,算法首先递归应用求两维点集凸包的Graham扫描法,在原始区域的点集中求出一系列的凸包,同时原始两维区域也被这些凸包划分为多个独立的子区域,然后对相邻两个凸包之间的子区域进行三角剖分,从而实现对整个原始两维区域的三角剖分.和以往得算法相比,提出的算法的时间效率大大提高了,并且在作者参与的军队2110建设项目应用中也体现了良好的效果.     

On sorting triangles in a delaunay tessellation

In a two-dimensional Delaunay-triangulated domain, there exists a partial ordering of the triangles (with respect to a vertex) that is consistent with the two-dimensional visibility of the triangles from that vertex. An equivalent statement is that a polygon that is star-shaped with respect to a given vertex can be extended, one triangle at a time, until it includes the entire domain. Arbitrary planar triangulations do not possess this useful property which allows incremental processing of the triangles.This work was partially supported by the National Science Foundation's US-Italy Collaborative Research Program under Grant INT-8714578 and Information, Robotics, and Intelligent Research Grant IRI-8704781.     

基于Delaunay三角剖分密度度量的聚类算法

针对K-means聚类算法无法正确识别非凸形状簇的缺陷,提出一种基于Delaunay三角剖分密度度量的聚类方法,利用Delaunay三角剖分图的最近性、邻接性等优良特性来反映数据自身特点并进行密度度量,同时以混沌优化方法实现聚类目标函数的全局优化,达到全局最小解。实验结果证明,基于Delaunay三角剖分密度度量方式的聚类算法能发现任意非凸形状簇。     

Delaunay三角网剖分的约束边嵌入改进算法

重点研究约束边强行嵌入D-三角网的问题。约束边嵌入是解决D-三角网转变为CD-三角网的一种非常有效的方法,而CD-三角网才能真实地虚拟地形地貌。针对基于凸凹判定的对角线交换算法存在的缺陷,提出"分裂约束边"的思想完善算法的健壮性,并引入快速点定位算法以提高算法的执行效率。