基于改进Voronoi区域面积的Mark Meyer曲率估算方法

以微分几何曲率计算公式为理论基础,对常用的Mark Meyer离散点云曲率估算方法进行改进,提出基于Voronoi区域面积的改进Mark Meyer算法。针对Mark Meyer算法中Voronoi区域面积的计算进行改进,对于Voronoi区域中存在钝角的情形进行详细论述并且改进钝角三角形的计算公式,同时给出更为准确的面积计算方法。将该算法应用于球面、柱面、抛物面、马鞍面,计算结果表明该算法提高了离散点云曲率估算的精度和稳定性。     

综合曲率约束的在线网格模型分割方法研究

针对现有三角网格模型块分割方法普遍存在计算复杂度高,无法体现工程意义,综合效果不理想,不满足Web环境下高效快速分割等问题,提出一种面向Web环境的简单高效的三角网格模型分割方法。根据高斯曲率和平均曲率特性划分出网格模型的凹区域,在凹区域中依据最小负曲率阈值提取凹特征区域,结合区域中心特征线提取方法以及边界线闭合和优化算法构造出闭合分割线,通过分割线将三角网格模型分割成有意义的分块。依托开源数字几何处理软件MeshLabJS,运用WebGL的几何处理及图形渲染功能,在普林斯顿标准数据集和COSEG形状数据集上进行算法测试,验证所提方法能够在Web环境下快速、高效、有意义地分割三维模型。     

基于离散曲率的三角形折叠简化算法

以三角形折叠算法为基础，提出了一种新的基于离散曲率的三角网格简化算法。该算法以网格表面的加权离散曲率为依据，对三角形进行折叠操作，给出了基于离散曲率和球面近似的新顶点的获取方法。实验结果证明了本文算法的有效性。     

三角网格模型顶点法矢与离散曲率计算

给出了一种新的面积角度加权的三角网格模型顶点法矢计算公式,在此基础上对Taubin离散曲率计算方法做了改进,采用质心距离权重代替面积权重,提出了新的离散曲率计算方法。实例表明,与原有公式及方法相比,该公式与方法的计算结果更为准确。     

基于NURBS曲面拟合的三角网格曲率计算

针对三角网格提出了一种基于NURBS曲面拟合的计算Gauss曲率和平均曲率的算法。首先选取边界检测后的二阶邻点作为局部拟合数据,采用直接投影法实现参数化,由二次NURBS曲面进行最小平方拟合反算控制点矩阵,最后由拟合曲面计算曲率。并从三角网格分辨率和噪声两方面进行了比较,实验结果表明本文算法精度高、较其他算法稳定,因而更具通用性。     

参数平面二叉树剖分网格简化

为了快速地对3维网格模型进行简化,提出了一种曲率自适应的3维网格简化算法,该算法首先将原始网格投影至参数平面上,并构造反映原始网格曲率分布的平面曲率灰度分布,用以表征简化过程中对网格各部分不同的采样密度要求;然后根据等曲率灰度分割的原则来对参数平面进行二叉树剖分,以构造反映其不均匀分布的非均衡二叉树结构,并依此选取简化后的网格顶点集合,以构造简化的三角网格.该算法的优点是执行速度快,同时在简化过程中仍能充分保持原始网格的细节.     

一种基于曲率的边折叠简化算法

针对目前三角网格简化算法在低分辨率的状态下往往丢失模型重要几何特征,从而导致视觉上的失真问题,提出了一种改进的边折叠三角网格简化算法。在Garland算法基础上引入了近似曲率的概念,并将其加入到二次误差测度中,使得二次误差测度在能够度量距离偏差的情况下,能够反映模型局部表面几何变化。实验结果表明改进的算法有效保持了模型的细节特征,简化效果更好。     

综合平均曲率与网格边的特征线提取方法

随着数字几何获取技术的发展,大量的复杂形体采用网格模型表示。而网格模型的特征线或特征边缘的识别和提取是后续开展几何和特征识别的基础工作,为此提出一种综合平均曲率与网格边的三角网格模型特征线提取方法。分两次提取:首先利用三角面片法矢夹角大小对模型中的尖锐边进行初次提取特征点;然后综合平均曲率与网格边的关系对特征点进行二次提取;最后用两次提取边的顶点作为特征点,进行分类分组处理拟合成特征线。经过实例验证,该算法可以快速地提取尖锐边和过渡边等,具有很好的提取效果。     

二维自适应前沿推进网格生成

针对二维平面问题,通过曲率计算和基于中轴理论的邻近特征计算控制区域边界曲线的离散;修改经典的前沿推进算法,利用边界驱动的单元尺寸控制方式在区域内部布置疏密过渡合理的三角网格;结合几何和拓扑策略提升网格质量。实验表明,上述算法可生成单元质量高、尺寸过渡合理的计算网格。     

用反调和平均曲率流实现网格保特征平滑

利用反调和平均曲率流，提出一种各向异性、快速的不规则三角网格去噪算法．模型中选择的各向异性演化权函数比较简单，同时保持了网格的几何特征．分别用显式格式和半隐式格式实现了此平滑算法．提供的数值例子显示了模型的有效性．     

Exact and interpolatory quadratures for curvature tensor estimation

The computation of the curvature of smooth surfaces has a long history in differential geometry and is essential for many geometric modeling applications such as feature detection. We present a novel approach to calculate the mean curvature from arbitrary normal curvatures. Then, we demonstrate how the same method can be used to obtain new formulae to compute the Gaussian curvature and the curvature tensor. The idea is to compute the curvature integrals by a weighted sum by making use of the periodic structure of the normal curvatures to make the quadratures exact. Finally, we derive an approximation formula for the curvature of discrete data like meshes and show its convergence if quadratically converging normals are available.     

三角形网格模型顶点曲率的求解算法*

推导了一般三角形网格模型顶点的平均曲率、高斯曲率和主曲率的计算方法,考虑到经常遇到粗糙三角形网格模型,为提高其曲率计算方法的精度,结合Loop细分曲面算法,进一步拓展了该曲率计算方法.该算法用于具有特征保持的网格模型简化取得了良好的效果.     

Detection of Salient Curvature Features on Polygonal Surfaces

We develop an approach for stable detection of perceptually salient curvature features on surfaces approximated by dense triangle meshes. The approach explores an "area degenerating" effect of the focal surface near its singularities and combines together a new approximations of the mean and Gaussian curvatures, nonlinear averaging of curvature maps, histogram-based curvature extrema filtering, and an image processing skeletonization procedure adapted for triangular meshes. Finally we use perceptually significant curvature extrema triangles to enhance the Garland-Heckbert mesh decimation method.     

三角网格特征边识别的一种有效方法

三角网格特征边识别在数字几何处理和计算机辅助制造(CAM)的模具加工中都有 着广泛的应用,该文指出了近年来有关网格特征边识别算法的各种弊端及原因,给出了一种鲁 棒的网格特征边识别新算法。该算法以网格特征点的识别为基础,能够识别以往算法常遗漏的 一些二面法向夹角比较小的网格边,增强了对C1 不连续网格边的识别能力。众多数值例子支 持了这个结论。     

A local tangential lifting differential method for triangular meshes

In this note we present a local tangential lifting (LTL) algorithm to compute differential quantities for triangular meshes obtained from regular surfaces. First, we introduce a new notation of the local tangential polygon and lift functions and vector fields on a triangular mesh to the local tangential polygon. Then we use the centroid weights proposed by Chen and Wu [4] to define the discrete gradient of a function on a triangular mesh. We also use our new method to define the discrete Laplacian operator acting on functions on triangular meshes. Higher order differential operators can also be computed successively. Our approach is conceptually simple and easy to compute. Indeed, our LTL method also provides a unified algorithm to estimate the shape operator and curvatures of a triangular mesh and derivatives of functions and vector fields. We also compare three different methods : our method, the least square method and Akima’s method to compute the gradients of functions.     

A local tangential lifting differential method for triangular meshes

In this note we present a local tangential lifting (LTL) algorithm to compute differential quantities for triangular meshes obtained from regular surfaces. First, we introduce a new notation of the local tangential polygon and lift functions and vector fields on a triangular mesh to the local tangential polygon. Then we use the centroid weights proposed by Chen and Wu [4] to define the discrete gradient of a function on a triangular mesh. We also use our new method to define the discrete Laplacian operator acting on functions on triangular meshes. Higher order differential operators can also be computed successively. Our approach is conceptually simple and easy to compute. Indeed, our LTL method also provides a unified algorithm to estimate the shape operator and curvatures of a triangular mesh and derivatives of functions and vector fields. We also compare three different methods : our method, the least square method and Akima’s method to compute the gradients of functions.     

Geometric accuracy analysis for discrete surface approximation

In geometric modeling and processing, computer graphics and computer vision, smooth surfaces are approximated by discrete triangular meshes reconstructed from sample points on the surfaces. A fundamental problem is to design rigorous algorithms to guarantee the geometric approximation accuracy by controlling the sampling density. This paper gives explicit formulae to the bounds of Hausdorff distance, normal distance and Riemannian metric distortion between the smooth surface and the discrete mesh in terms of principle curvature and the radii of geodesic circum-circle of the triangles. These formulae can be directly applied to design sampling density for data acquisitions and surface reconstructions. Furthermore, we prove that the meshes induced from the Delaunay triangulations of the dense samples on a smooth surface are convergent to the smooth surface under both Hausdorff distance and normal fields. The Riemannian metrics and the Laplace–Beltrami operators on the meshes are also convergent to those on the smooth surfaces. These theoretical results lay down the foundation for a broad class of reconstruction and approximation algorithms in geometric modeling and processing.Practical algorithms for approximating surface Delaunay triangulations are introduced based on global conformal surface parameterizations and planar Delaunay triangulations. Thorough experiments are conducted to support the theoretical results.