基于黎曼度量的复杂参数曲面有限元网格生成方法

给出了三维空间的黎曼度量和曲面自身的黎曼度量相结合的三维复杂参数曲面自适应网格生成的改进波前推进算法.详细阐述了曲面参数域上任意一点的黎曼度量的计算和插值方法;采用可细化的栅格作为背景网格,在降低了程序实现的难度的同时提高了网格生成的速度;提出按层推进和按最短边推进相结合的方法,在保证边界网格质量的同时,提高曲面内部网格的质量.三维自适应黎曼度量的引入,提高了算法剖分复杂曲面的自适应性.算例表明,该算法对复杂曲面能够生成高质量的网格,而且整个算法具有很好的时间特性和可靠性.     

几何自适应参数曲面网格生成

为满足有限元分析的需要,针对参数曲面提出一种几何自适应的网格生成方法.通过黎曼度量控制下的曲面约束Delaunay三角化获得曲面中轴,将其用于自动识别曲面邻近特征,并通过曲率计算自动识别曲率特征;根据邻近特征和曲率特征,融合传统网格尺寸控制技术控制边界曲线离散,并创建密度场;结合映射法和前沿推进技术对组合参数曲面生成几何自适应的网格.实验结果表明,该方法能够处理复杂的几何外形,生成的网格具有很好的自适应效果和质量.     

基于Gregory法的N边域非自交结构网格生成

提出了在N边域裁减参数曲面上生成非自交光顺结构网格的方法。利用Gregory法,首先在参数坐标平面上将N边域分成N个四边形区域,并生成每个四边形区域内的网格,然后通过映射获得三维空间上的网格坐标点。为消除映射过程中所产生的自交现象,根据非自交特性和光顺网格的特点,提出了优化目标函数,并采用共轭梯度数值解法获得了最优解。利用该方法可以获得N边域上非自交光顺的结构网格,拓展了N边域裁剪曲面的使用范围。     

边界面法数值结果的云图表征

边界面法继承了传统边界元法的优点,并将几何实体的边界曲面离散为参数空间里的曲面单元,在处理一些特殊问题如移动边界、高梯度、大变形等方面显示出特殊的优越性。但是也使得计算结果的后处理遇到困难。提出了一种基于黎曼度量推进波前法生成三角背景网格的实用边界面法计算结果后处理方法。该法对求解域剖分成三角背景网格然后将计算结果映射到网格节点上,通过区域填充获得计算结果的云图。温度场的数值算例表明该方法可靠实用。     

四边形网格生成中的前沿边生长改进算法

为提高B样条曲面重构中点云四边形网格的生成效率和质量,对现有的四边形网格Q-Morph前沿边生长算法进行改进,提出面向四边形网格生成的三角网格拓扑优化方法,通过设定生长限制条件和调整网格顶点度,保证全局四边形网格质量,实现适合复杂曲面重构的规则四边形网格获取。实例结果表明,该算法效率高、适应性好,生成的四边形网格具有分布均匀、不规则网格数量少的优点。     

基于C—C细分的四边形网格上插值光滑Bezier曲面的生成

在任意拓扑的四边形网格上构造光滑的曲面是计算机辅助几何设计中的一个重要问题．基于C—C细分,提出一种从四边形网格上生成插值网格顶点的光滑Bezier曲面片的算法．将输入四边形网格作为C—C细分的初始控制网格,在四边形网格的每张面上对应得到一张Bezier曲面,使Bezier曲面片逼近C—C细分极限曲面．曲面片在与奇异顶点相连的边界上G^1连续,其他地方C^2连续．为解决C—C细分的收缩问题,给出了基于误差控制的迭代扩张初始控制网格的方法,使从扩张后网格上生成的曲面插值于初始控制网格的顶点．实验结果表明,该算法效率高,生成的曲面具有较好的连续性,适用于对四边化后的网格模型上重建光滑的曲面．     

基于C-C细分的四边形网格上插值光滑Bézier曲面的生成

在任意拓扑的四边形网格上构造光滑的曲面是计算机辅助几何设计中的一个重要问题.基于C-C细分,提出一种从四边形网格上生成插值网格顶点的光滑Bézier曲面片的算法.将输入四边形网格作为C-C细分的初始控制网格,在四边形网格的每张面上对应得到一张Bézier曲面,使Bézier曲面片逼近C-C细分极限曲面.曲面片在与奇异顶点相连的边界上G1连续,其他地方C2连续.为解决C-C细分的收缩问题,给出了基于误差控制的迭代扩张初始控制网格的方法,使从扩张后网格上生成的曲面插值于初始控制网格的顶点.实验结果表明,该算法效率高,生成的曲面具有较好的连续性,适用于对四边化后的网格模型上重建光滑的曲面.     

多裁剪自由曲面生成有限元网格的实现

论述了多裁剪自由曲面生成有限元曲面网格的几个关键技术.采用了推进波前法生成曲面网格,给出了核心算法;在曲面算法中运用了介于参数法与直接法之间的新方法.针对求解曲面上最优点的参数域反算问题,引入了切矢逆求方法,可使迭代次数大为降低.测试表明,该算法快速、稳定.对大型的多裁剪自由曲面生成的曲面有限元网格,可直接用于有限元计算.     

形状空间中模型变化的插值生成

在形状空间中,由三角形网格构成的模型可视为空间中的一个点,可以借助黎曼度量对形状空间进行操作,从而实现对模型的变换.改进了已有的操纵形状空间的方法,根据输入模型顶点的位置变化判断是否需要利用黎曼度量计算插值位置,降低了形状空间的维数,提高了运算速度.实验结果显示,混合利用线性插值和利用形状空间计算2种方法的生成模型具有良好的效果.     

边界优先的Delaunay-层推进曲面四边形网格生成

为了提高复杂组合曲面四边形网格生成的鲁棒性和边界单元质量,提出一种边界优先的Delaunay-层推进网格生成方法.首先在剖分域内粗的约束Delaunay背景网格的辅助下,以物理域的位置偏差为引导,在参数域中迭代计算边界点的法矢量;然后结合层推进策略,在几何特征附近生成各向异性或各向同性正交网格;最后使用Coring技术加速内部网格的生成并进行单元合并,得到四边形为主的网格.若干复杂平面区域和组合曲面模型的剖分结果表明,所提方法可生成等角扭曲度和纵横比优于主流商业软件的网格;在12个线程的PC平台上,使用OpenMP并行剖分包含21 772张曲面的引擎模型只用了38.68 s.     

Anisotropic surface meshing with conformal embedding

This paper introduces a parameterization-based approach for anisotropic surface meshing. Given an input surface equipped with an arbitrary Riemannian metric, this method generates a metric-adapted mesh with user-specified number of vertices. In the proposed method, the edge length of the input surface is directly adjusted according to the given Riemannian metric at first. Then the adjusted surface is conformally embedded into a parametric 2D domain and a weighted Centroidal Voronoi Tessellation and its dual Delaunay triangulation are computed on the parametric domain. Finally the generated Delaunay triangulation can be mapped from the parametric domain to the original space, and the triangulation exhibits the desired anisotropic property. We compute the high-quality remeshing results for surfaces with different types of topologies and compare our method with several state-of-the-art approaches in anisotropic surface meshing by using the standard measurement criteria.     

Intrinsic Polynomials for Regression on Riemannian Manifolds

We develop a framework for polynomial regression on Riemannian manifolds. Unlike recently developed spline models on Riemannian manifolds, Riemannian polynomials offer the ability to model parametric polynomials of all integer orders, odd and even. An intrinsic adjoint method is employed to compute variations of the matching functional, and polynomial regression is accomplished using a gradient-based optimization scheme. We apply our polynomial regression framework in the context of shape analysis in Kendall shape space as well as in diffeomorphic landmark space. Our algorithm is shown to be particularly convenient in Riemannian manifolds with additional symmetry, such as Lie groups and homogeneous spaces with right or left invariant metrics. As a particularly important example, we also apply polynomial regression to time-series imaging data using a right invariant Sobolev metric on the diffeomorphism group. The results show that Riemannian polynomials provide a practical model for parametric curve regression, while offering increased flexibility over geodesics.     

Discrete surface Ricci flow

This work introduces a unified framework for discrete surface Ricci flow algorithms, including spherical, Euclidean, and hyperbolic Ricci flows, which can design Riemannian metrics on surfaces with arbitrary topologies by user-defined Gaussian curvatures. Furthermore, the target metrics are conformal (angle-preserving) to the original metrics. A Ricci flow conformally deforms the Riemannian metric on a surface according to its induced curvature, such that the curvature evolves like a heat diffusion process. Eventually, the curvature becomes the user defined curvature. Discrete Ricci flow algorithms are based on a variational framework. Given a mesh, all possible metrics form a linear space, and all possible curvatures form a convex polytope. The Ricci energy is defined on the metric space, which reaches its minimum at the desired metric. The Ricci flow is the negative gradient flow of the Ricci energy. Furthermore, the Ricci energy can be optimized using Newton's method more efficiently. Discrete Ricci flow algorithms are rigorous and efficient. Our experimental results demonstrate the efficiency, accuracy and flexibility of the algorithms. They have the potential for a wide range of applications in graphics, geometric modeling, and medical imaging. We demonstrate their practical values by global surface parameterizations.     

基于STEP的三维表面有限元网格生成技术的研究*

研究了三维表面有限元网格自动生成的技术,利用映射法实现了模型表面的三角网格剖分。基于STEP文件格式的模型的导入和重建,将模型的每个表面映射至参数空间,利用推进波前法生成参数面网格,然后映射回三维表面。研制了一套网格剖分策略,运用该策略对多种类型表面进行了分析求解。     

Metric learning for text documents

Many algorithms in machine learning rely on being given a good distance metric over the input space. Rather than using a default metric such as the Euclidean metric, it is desirable to obtain a metric based on the provided data. We consider the problem of learning a Riemannian metric associated with a given differentiable manifold and a set of points. Our approach to the problem involves choosing a metric from a parametric family that is based on maximizing the inverse volume of a given data set of points. From a statistical perspective, it is related to maximum likelihood under a model that assigns probabilities inversely proportional to the Riemannian volume element. We discuss in detail learning a metric on the multinomial simplex where the metric candidates are pull-back metrics of the Fisher information under a Lie group of transformations. When applied to text document classification the resulting geodesic distance resemble, but outperform, the tfidf cosine similarity measure.     

Discrete heat kernel determines discrete Riemannian metric

The Laplace–Beltrami operator of a smooth Riemannian manifold is determined by the Riemannian metric. Conversely, the heat kernel constructed from the eigenvalues and eigenfunctions of the Laplace–Beltrami operator determines the Riemannian metric. This work proves the analogy on Euclidean polyhedral surfaces (triangle meshes), that the discrete heat kernel and the discrete Riemannian metric (unique up to a scaling) are mutually determined by each other. Given a Euclidean polyhedral surface, its Riemannian metric is represented as edge lengths, satisfying triangle inequalities on all faces. The Laplace–Beltrami operator is formulated using the cotangent formula, where the edge weight is defined as the sum of the cotangent of angles against the edge. We prove that the edge lengths can be determined by the edge weights unique up to a scaling using the variational approach.The constructive proof leads to a computational algorithm that finds the unique metric on a triangle mesh from a discrete Laplace–Beltrami operator matrix.