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Efficient Computation of Clipped Voronoi Diagram for Mesh Generation

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Efficient Computation of Clipped Voronoi Diagram for Mesh Generation Efficient Computation of Clipped Voronoi Diagram for Mesh Generation Dong-Ming Yana,b,c, Wenping Wanga, Bruno L´evyb, Yang Liub,d aDepartment of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong bProject ALICE, INRIA/LORIA, Campus scientifique 615, rue du Jardin Botanique, 54600, Villers les Nancy, France cGeometric Modeling and Scientific Visualization Center, KAUST, Thuwal 23955-6900, Kingdom of Saudi Arabia dMicrosoft Research Asia, Building 2, No. 5 Danling Street, Haidian District, Beijing, 100800, P.R. China Abstract The Voronoi diagram is a fundamental geometric structure widely used in various fields, especially in computer graphics and geometry computing. For a set of points in a compact domain (i.e. a bounded and closed 2D region or a 3D volume), some Voronoi cells of their Voronoi diagram are infinite or partially outside of the domain, but in practice only the parts of the cells inside the domain are needed, as when computing the centroidal Voronoi tessellation. Such a Voronoi diagram confined to a compact domain is called a clipped Voronoi diagram. We present an efficient algorithm to compute the clipped Voronoi diagram for a set of sites with respect to a compact 2D region or a 3D volume. We also apply the proposed method to optimal mesh generation based on the centroidal Voronoi tessellation. Keywords: clipped Voronoi diagram, Delaunay triangulation, centroidal Voronoi tessellation, mesh generation. 1. Introduction computing the clipped Voronoi diagram with re- spect to a complicated input domain is a difficult The Voronoi diagram is a fundamental geometric problem and there is no efficient solution in the ex- structure which has numerous applications in var- isting literature. There has been no previous work ious fields, such as shape modeling, motion plan- on computing the exact clipped Voronoi diagram ning, scientific visualization, geography, chemistry, for non-convex domains with arbitrary topology. A biology and so on. brute-force implementation would be inefficient be- Suppose that a set of sites in a compact domain in cause of the complexity of the domain. d is given. Each site is associated with a Voronoi R The motivation of the work is inspired by the re- cell containing all the points in d closer to the site R cent work [23, 38]. They showed in [23] that the than to any other sites; these cells constitute the CVT energy function is C2-continuous, which can Voronoi diagram of the set of sites. Voronoi cells of be minimized by the Newton-like algorithm, such those sites on the convex hull are infinite, and some as the L-BFGS method presented. In [38], an effi- of Voronoi cells may be partially outside of the spec- cient CVT-based surface remeshing algorithm was ified domain. However, in many applications one presented with an exact algorithm for computing usually needs only the parts of Voronoi cells inside the restricted Voronoi diagram on mesh surfaces. the specific domain. That is, the Voronoi diagram In this paper, we aim at applying the fast CVT restricted to the given domain, which is defined as remeshing framework to 2D/3D mesh generation. the intersection of the Voronoi diagram and the do- To minimize the CVT energy function, one needs to main, and is therefore called the clipped Voronoi compute the clipped Voronoi diagram in the input diagram [7]. The corresponding Voronoi cells are domain for function evaluation and gradient com- called the clipped Voronoi cells (see Figure 1). putation (see Section 2). Computing the clipped Voronoi diagram in a con- vex domain is relatively easy { one just needs to In this paper, we shall present practical algo- compute the intersection of each Voronoi cell and rithms for computing clipped Voronoi diagrams the domain, both being convex. However, directly based on several simple operations. The main idea Preprint submitted to Computer-Aided Design October 20, 2011 Voronoi diagram of surfaces/volumes. It is natu- ral to use the geodesic metric to define the so called Geodesic Voronoi Diagram (GVD) on sur- faces. Kunze et al. [20] presented a divide-and- conquer algorithm of computing GVD for paramet- ric surfaces. Peyr´eand Cohen [28] used the fast marching algorithm to compute a discrete approx- imated GVD on a mesh surface. However, the cost of computing the exact GVD on surfaces is high, (a) (b) for instance, the fast marching method requires to Figure 1: Examples of clipped Voronoi diagram in solve the nonlinear Eikonal equation. a circle (a) and a cylinder (b). The clipped Voronoi The restricted Voronoi diagram (RVD) [14] is de- cells on the boundary are shaded. fined as the intersection of the 3D Voronoi diagram and the surface, which is applied for computing constrained/restricted CVT on continuous surfaces of our approach is that instead of computing the by Du et al. [13]. The concept of the constrained intersection of Voronoi diagram and the domain di- CVT was extended to mesh surfaces in recent work rectly, we first detect the Voronoi cells that have in- [23, 38] and applied for isotropic surface remesh- tersections with domain boundary and then apply ing. Yan et al. [38] proposed an exact algorithm to computation for those cells only. We use a simple construct the RVD on mesh surfaces which consist and efficient algorithm based on connectivity propa- of triangle soups. They processed each triangle in- gation for detecting the cells that intersect with the dependently where a kd-tree was used to find the domain boundary (i.e., polygons in 2D and mesh nearest sites of each triangle in order to identify its surfaces in 3D, respectively). We also utilize the incident Voronoi cells and compute the intersection. presented techniques for mesh generation as appli- In this paper, we further improve the efficiency of cations. The contributions of this paper include : the RVD computation by applying a neighbor prop- agation approach instead of using kd-tree query, as- • introduce new methods for computing the suming the availability of the mesh connectivity in- clipped Voronoi diagram in 2D regions (Sec- formation (Section 4.1). tion 3) and 3D volumes (Section 4); The clipped Voronoi diagram is defined as the intersection of the 3D (resp. 2D) Voronoi diagram • present practical algorithms for 2D/3D mesh and the given 3D volume (resp. a 2D region). Chan generation based on the presented clipped et al. [7] introduced an output-sensitive algorithm Voronoi diagram computation techniques (Sec- for constructing the 3D clipped Voronoi diagram tion 5). of a convex polytope. Kyons et al. [24] presented an O(nlog(n)) algorithm to compute the clipped Voronoi diagram in a 2D square and applied it 1.1. Previous work to network visualization. Yan et al. [39] utilized the clipped Voronoi diagram to compute the peri- The properties of the Voronoi diagram have been odic CVT in 2D periodic space. Hudson et al. [18] extensively studied in the past decades. Existing computed the 3D clipped Voronoi diagram in the techniques compute the Voronoi diagram for point bounding box of the sites and used it to improve the sites in 2D and 3D Euclidean spaces efficiently. time and space complexities of computing the full There are several robust implementations that are persistent homological information. However, the publicly available, such as CGAL [1] and Qhull [6]. handling of non-convex objects was not addressed A thorough survey of the Voronoi diagram is out in these approaches. Existing algorithms used a dis- of the scope of this paper, the reader is referred to crete approximation in specific applications. Hoff [5, 15, 26] for details of theories and applications of III et al. [17] proposed a method for computing the the Voronoi diagram. We shall restrict our discus- discrete generalized Voronoi diagram using graph- sion to the approaches of computing the Voronoi ics hardware. The Voronoi diagram computation diagram restricted to a specific 2D/3D domain and was formulated as a clustering problem in the dis- their applications. crete voxel/pixel space. Sud et al. [33] presented an 2 n-body proximity query algorithm based on com- relationship between element qualities and interpo- puting the discrete 2nd order Voronoi diagram on lation error/condition number. In the following, we the GPU. GPU-based algorithms were fast but pro- briefly discuss the main categories of tetrahedral duced only a discrete approximation of the true meshing. Voronoi diagram. In this paper, we shall present efficient algorithms to compute the exact clipped • The octree-based approaches (e.g. [10, 40]) Voronoi diagram for both 2D and 3D domains. subdivide the bounding box of input model repeatedly until a pre-specified resolution is Mesh generation. Mesh generation has been ex- reached, then connect those cells to form the tensively studied in meshing community over past tetrahedra. In general, this kind of approaches decades. The detailed reviews of mesh genera- cannot prevent bad elements near the bound- tion techniques are available in [27, 16]. In the ary. following, we will focus on the work based on Voronoi/Delaunay concepts, which are most related • Advancing front methods start from the do- to ours. We also briefly review the main categories main boundary and stuff the interior of the do- of tetrahedral mesh generation techniques. main progressively, guided by specified heuris- The concept of Voronoi diagram has been suc- tic to control the shape/size. Advancing front cessfully used for meshing and analyzing point data. methods are fast but a high-quality triangu- Amenta et al. [4] presented a new surface recon- lated boundary is required. struction algorithm based on Voronoi filtering. This • Delaunay/Voronoi based approaches generate algorithm has provable guarantees when the sam- meshes satisfying Delaunay properties, which ple points of a smooth surface satisfy the lfs (local maximize the minimal angle of shape elements.
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