反向工程中三角网格的加工特征识别

为实现反向工程中的特征重构,提出一种在三角网格上由Morse-Smale复形转换为分割面属性邻接图进行加工特征提取的算法.首先通过移动最小二乘曲面法计算网格顶点曲率,构建曲率特性指标函数,消除因网格连接引起的误差,达到降噪的作用;然后在三角网格上建立和简化Morse-Smale复形,精确提取特征线,将网格分割为边界清晰连续的区域,并获得各区域的邻接关系;最后判断关键点和特征线的性质,将Morse-Smale复形直接转换为分割面属性邻接图进行加工特征识别.实验结果表明,该算法准确、高效,而且不需要人工干预,对于反向工程中大规模、带噪声的网格具有较好的识别效果.     

三角网格模型的特征线提取

在反求工程中，散乱数据点云的曲面重构常采用三角网格模型，若将其转换成曲面实体模型则有更广泛的应用，从三角网格模型中提取特征线是转换过程中的重要步骤．在讨论反求工程中数据点云分块方法的基础上，采取“基于边”的方法来提取特征线：先提取特征点，再连接成特征线．根据相邻三角片的法矢夹角和各点主曲率是否为极值，分两次提取特征点，利用三角顶点加权和均匀化等方法减少狭长三角片对特征点提取的计算误差影响，再将特征点分组连接成B样条曲线．文中算法的结果可为B样条曲面分片拟合和建立B-rep曲面实体模型提供依据。     

三维重建网格模型的缺陷孔洞识别与修复方法

针对三维重建网格模型经常出现异常缺失孔洞的问题,提出一种缺陷孔洞自动识别与孔洞区域细节特征保持的曲面修复方法。首先对缺失区域的上下文及轮廓曲线进行异常检测以判断是否为缺陷孔洞, 确认为缺陷孔洞后对孔洞周边的特征线进行检测与匹配构造孔洞区域的基曲面;之后引进一个无约束的三角剖分对基曲面进行三角化;最后利用网格的各向异性进行细化及形态调整,改善网格的拓扑结构和几何性质。实验结果表明,该方法能够有效地识别三角网格模型的缺陷孔洞区域并还原其细节特征。     

基于八叉树空间分割的NURBS曲面重构方法

对三维模型和点云曲面重构方法进行深入研究,根据应用特点提出八叉树空间分割和N U RBS曲面重构方法。利用八叉树的快速收敛特性对三维实体的点云数据进行分割、精简,采用N U RBS方法对局部网格曲面进行重构;采用八叉树和四叉树相混合的数据结构,渐进地进行网格曲面的重构。存储结构采用扩展式八叉树结构,编码采用8进制前缀编码方法。利用O penG L设计一个实验模型系统验证了该算法的可行性和有效性。     

岩石三维图像裂缝提取方法

提出一种新的岩石三维图像裂缝提取算法。首先对三维岩石孔隙模型的每个连通分量执行表面重建、拉普拉斯网格平滑、网格简化等操作。根据三角网格面积和网格单位法向量方向特征,将三角网格划分为不同类别。利用形状因子判定每个三角网格类构成的三维空间结构是否具有裂缝特征。对具有裂缝特征的三维空间结构所包含的体素点集执行形态学膨胀操作,并与原始三维岩石孔隙模型连通分量的体素点集进行逻辑与操作,与操作结果即岩石裂缝。实验结果表明,该方法具有较好的裂缝提取效果。     

面向可展特征的网格模型去噪方法

可展特征是三维网格模型的常见几何特征。为了更好地对具备可展特征的网格模型进行去噪,提出一种面向可展特征的网格模型去噪方法。首先基于变分形状逼近策略分割可展区域,识别出网格模型上可展特征区域,并对分割区域进行基于可展性度量的合并和划分,改进现有 L 0 去噪算法中针对非均匀噪声网格的正则优化表达项,引入三角网格顶点的可展度量项,利用可展特征的曲面法向量 L 0 范数的优化问题求解实现网格模型的去噪。通过对多个模型数据集中的大量模型数据进行处理,验证了该方法的有效性。实验表明,结合模型的可展特性的去噪方法在保持模型的几何特征特别是可展特征上效果优于已有方法。     

一种散乱点云空间直接剖分算法

散乱点云的三角剖分在曲面重建中发挥着重要作用。在对三角剖分基本方法深入分析的基础上对此类点云提出了一种高效的重构算法。本算法将基于动态球策略的搜索算法引入到曲面重建中,源于增量式计算的思想,结合约束准则和设计的顶点度量函数,从基础三角面片开始扩展到覆盖整个物体表面。分析及实验结果表明,该算法能有效地对点云数据进行三角网格化,同时剖分后的三角网格曲面最大限度地保持了原有曲面的特性,证明了提出的基于动态球的曲面重构算法应用于散乱点云曲面重构问题的可行性。     

基于RBF神经网络的点云数据曲面重建快速算法

在分析现有重构方法局限性的基础上,给出了一种基于神经网络的点云数据重构三维网格形状的快速算法。首先对点云数据进行归一化处理;然后进行特征线提取,并以特征线为基础对曲面进行分割。该方法能直接从神经网络的权值矩阵得到曲线的控制顶点或曲面的控制网格,通过神经网络的权值约束实现曲线段或曲面片之间的连接。实验结果表明,使用该方法能快速获得形状良好的网格曲面。     

点云数据重构三维网格形状的新算法

在分析现有重构方法局限性的基础上,提出了一种基于神经网络的点云数据重构三维网格形状的新算法。首先对点云数据平滑处理;然后进行特征线提取,并以特征线为基础对曲面进行分割。该方法能直接从神经网络的权值矩阵得到曲线的控制顶点/曲面的控制网格,通过神经网络的权值约束实现曲线段/曲面片之间的光滑拼接。能显著提高逼近网格的品质,从而实现了点云数据的精确曲面重构,实际的算例结果表明该方法实用可靠。     

汽车发动机风扇叶片的逆向造型与分析

以汽车发动机风扇为研究对象,采用ATOS光学测量系统对风扇表面数字化得到其三角网格模型,利用三角网格数据预处理技术、过渡曲面特征提取方法、三维重构及实体造型原理对叶片进行逆向造型,并对重构的模型进行光顺性和精度校检。获得符合设计要求的叶片实体模型。     

散乱点云的三角网格重构

基于增量扩散法的思想,提出并实现了一个散乱点云的三角网格重构算法,算法首先利用体素网格的散列表对散乱点进行组织,然后在确定了初始种子三角形的基础上,基于活动边扩展规则构造新的三角形,使网格不断向周围扩展直到活动边表空为止,最后算法合并棱边并计算每个三角形的顶点法矢,最终构造出散乱点云的三角网格。     

Stable Real-Time Surgical Cutting Simulation of Deformable Objects Embedded with Arbitrary Triangular Meshes

Surgical simulators need to simulate deformation and cutting of deformable objects. Adaptive octree mesh based cutting methods embed the deformable objects into octree meshes that are recursively refined near the cutting tool trajectory. Deformation is only applied to the octree meshes; thus the deformation instability problem caused by degenerated elements is avoided. Biological tissues and organs usually contain complex internal structures that are ignored by previous work. In this paper the deformable objects are modeled as voxels connected by links and embedded inside adaptive octree meshes. Links swept by the cutting tool are disconnected and object surface meshes are reconstructed from disconnected links. Two novel methods for embedding triangular meshes as internal structures are proposed. The surface mesh embedding method is applicable to arbitrary triangular meshes, but these meshes have no physical properties. The material sub-region embedding method associates the interiors enclosed by the triangular meshes with physical properties, but requires that these meshes are watertight, and have no self-intersections, and their smallest features are larger than a voxel. Some local features are constructed in a pre-calculation stage to increase simulation performance. Simulation tests show that our methods can cut embedded structures in a way consistent with the cutting of the deformable objects. Cut fragments can also deform correctly along with the deformable objects.     

基于区域分割的三角网格模型相似性比较

为了在工程应用中检索已有的三角网格模型,以便重用相应零件的设计信息,节省设计和加工成本,提出一种基于区域分割技术的三角网格模型相似性比较算法。依据三角网格模型的球面图像将模型分割为若干个区域;对每个分割得到的区域用一个10维向量表达其形状的几何特征和拓扑特征,一个三角网格模型的特征即可通过各分割区域所对应的10维向量组成的向量组表达;将该向量组作为三角网格模型的形状描述子,两个三角网格模型的相似性可通过相对应的形状描述子间的相似性表达。将描述子中的每个向量看成是一个带有属性的节点,通过两组节点组成的完全二分图的最优匹配进行两个形状描述子之间的相似性比较,实现两个三角网格模型之间的相似性比较。实验结果表明,该算法有效可行。     

Segmentation of multi-material CT data of mechanical parts for extracting boundary surfaces

We introduce a method for segmentation of materials segmented in volumetric models of mechanical parts created by X-ray CT scanning for the purpose of generating their boundary surfaces. When the volumetric model is composed of two materials, one for the object and the other for the background (Air), these boundary surfaces can be extracted as isosurfaces using a surface contouring method. For a volumetric model composed of more than two materials, we need to classify the voxel types into segments by material and then use a surface contouring method that can deal with both CT values and material types. Here we propose a method for precisely classifying the volumetric model into its component materials using a modified and combined method of two well-known algorithms in image segmentation, region growing and Graph-cut. We then apply our non-manifold iso-contouring method to generate triangulated mesh surfaces. In addition, we demonstrate the effectiveness of our method by constructing high-quality triangular mesh models of the segmented parts.     

Extended linked voxel structure for point-to-mesh distance computation and its application to NC collision detection

When working with milling or polishing robots and large workpieces it is necessary to check not only the milling or polishing tool for collision, but it is also necessary to check the remaining arms of the robot for collision. In most of the cases the arms of the robot do not collide with the workpiece and so applying an existing collision detection algorithm to the arms of the robot slows the process down. In this paper, we present an algorithm for quickly assuring non-collisions, which is especially targeted at collisions of the arms of the robot with a workpiece. The algorithm is based on an extended voxel structure. More precisely, we extend a voxel structure by adding distance values to the corner of the voxels and by linking empty voxels to non-empty voxels to accelerate finding the desired voxel. This ensures that we only need to consider a small subset of the triangles describing the workpiece’s surface, namely those triangles that are close to the possible collision area. The triangles within each non-empty voxel are stored in a bsp-tree. For empty voxels, we save information about the distances to the mesh. This setup speeds up the point-to-mesh distance calculation, especially for points close to the mesh. The extra distance information in empty voxels enables a fast distance estimation and hence a fast early collision check.     

An approach to automated decomposition of volumetric mesh

Mesh decomposition is critical for analyzing, understanding, editing and reusing of mesh models. Although there are many methods for mesh decomposition, most utilize only triangular meshes. In this paper, we present an automated method for decomposing a volumetric mesh into semantic components. Our method consists of three parts. First, the outer surface mesh of the volumetric mesh is decomposed into semantic features by applying existing surface mesh segmentation and feature recognition techniques. Then, for each recognized feature, its outer boundary lines are identified, and the corresponding splitter element groups are setup accordingly. The inner volumetric elements of the feature are then obtained based on the established splitter element groups. Finally, each splitter element group is decomposed into two parts using the graph cut algorithm; each group completely belongs to one feature adjacent to the splitter element group. In our graph cut algorithm, the weights of the edges in the dual graph are calculated based on the electric field, which is generated using the vertices of the boundary lines of the features. Experiments on both tetrahedral and hexahedral meshes demonstrate the effectiveness of our method.     

Surface feature based mesh segmentation

Mesh segmentation has a variety of applications in product design, reverse engineering, and rapid prototyping fields. This paper presents a novel algorithm of mesh segmentation from original scanning data points, which essentially consists of three steps. Normal based initial decomposing is first performed to recognize plane features. Then we implement further segmentation based on curvature criteria and Gauss mapping, followed by the detection of quadric surface features. The segmentation refinement is finally achieved using B-spline surface fitting technology. The experimental results on many 3D models have demonstrated the effectiveness and robustness of the proposed segmentation method.     

On marching cubes

A characterization and classification of the isosurfaces of trilinear functions is presented. Based upon these results, a new algorithm for computing a triangular mesh approximation to isosurfaces for data given on a 3D rectilinear grid is presented. The original marching cubes algorithm is based upon linear interpolation along edges of the voxels. The asymptotic decider method is based upon bilinear interpolation on faces of the voxels. The algorithm of this paper carries this theme forward to using trilinear interpolation on the interior of voxels. The algorithm described here will produce a triangular mesh surface approximation to an isosurface which preserves the same connectivity/separation of vertices as given by the isosurface of trilinear interpolation.     

CAD mesh model segmentation by clustering

CAD mesh models have been widely employed in current CAD/CAM systems, where it is quite useful to recognize the features of the CAD mesh models. The first step of feature recognition is to segment the CAD mesh model into meaningful parts. Although there are lots of mesh segmentation methods in literature, the majority of them are not suitable to CAD mesh models. In this paper, we design a mesh segmentation method based on clustering, dedicated to the CAD mesh model. Specifically, by the agglomerative clustering method, the given CAD mesh model is first clustered into the sparse and dense triangle regions. Furthermore, the sparse triangle region is separated into planar regions, cylindrical regions, and conical regions by the Gauss map of the triangular faces and Hough transformation; the dense triangle region is also segmented by the mean shift operation performed on the mean curvature field defined on the mesh faces. Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.