基于广义基曲面参数化的N边域曲面重构

研究了由散乱数据点集重构N边域曲面的方法.已有方法大都使用单张B样条曲面进行拟合,或由用户手工描绘曲面片的边界曲线网格.与之不同,为便于数字处理,采用广义基曲面参数化方法可以在建立曲面片网格同时进行散乱数据的参数化,全过程无需人工干预.另外,推导了在曲面拟合算法中控制顶点约束的确定方法以满足给定的边界条件.通过仿真,研究了曲面片网格的光顺以在N边域曲面内部达到G1连续,并以实例证明了文中算法的有效性.     

残缺网格模型的快速B样条曲面重建

针对残缺的三角网格模型,提出一种将网格模型的散乱数据点转化为有序阵列点再进行B样条曲面快速重建的算法.首先确定最小二乘平面上的一个矩形参数域,再构造出一个平面阵列点列,并部分映射到三维网格上;然后利用空间阵列点的邻域信息估计4个角点的空间坐标,并构造径向基函数曲面,用于补充空间阵列点列中残缺的数据;最后利用有序点列拟合的高效性构造B样条曲面.实验结果表明:该算法速度快、拟合精度高、鲁棒性强,重建的曲面具有良好的光顺性和可延伸性,适用于逆向工程中对经过数据分割后的网格模型的自由曲面重建.     

点云模型基于几何特征增强与保特征曲面拟合的法向估计方法EI北大核心CSCD

现有法向估计中,尤其是模型中存在较大噪声的情况下,目标点邻域的选择是一个关键且困难的问题.针对点云模型,为了提高法向估计准确度,提出一种自适应选择邻域且保特征、抗噪声的法向估计方法.首先,提出双边非局部特征增强模块,根据网络前置学习特征以及邻域点几何特性对点邻域进行加权选择,并据此对局部特征进行增强,以提升网络对模型局部几何特征学习的能力;然后,采用局部特征与全局特征相结合的形式刻画点云完备的几何特征,并以此为基础进行局部曲面拟合及法向估计;最后,在局部曲面拟合中提出邻域保特征损失,依据邻域点受噪声干扰度对邻域点的拟合权重进行调整,实现保细节特征的局部曲面拟合,提高对噪声的鲁棒性.实验使用PCPNET数据集进行模型训练和测试,大量定性与定量的实验结果表明,与相关方法相比,所提方法对于不同噪声级别以及不同密度分布等复杂情形都可取得更加准确的法向估计结果,并更好地推动曲面重建等点云处理应用.     

三维网格曲面的可展性优化算法

可展曲面是每点高斯曲率均为0的曲面,具有许多良好的性质,因此在工业中具有很多应用.将一般的曲面用可展曲面来逼近表示具有现实意义.以此为目的,文中设计了一个有效的算法来处理一般的曲面,使得处理后的曲面尽可能满足可展的性质,同时与初始的曲面尽量地接近.首先利用最小范数方法来对网格曲面进行处理,得到初始预测的网格曲面.初始预测曲面具有良好的可展性,但是不能较好地保持网格的局部结构.然后利用尽可能刚性(As-rigid-as-possible)的方法,在初始预测曲面的基础上进行修正得到新的网格曲面.为了保持局部结构,作者的方法可以是基于顶点邻域的,也可以是基于三角形的.这两个过程可以迭代进行,直至得到满足要求的结果.与以往的算法相比,文中算法能保证结果收敛,迭代次数更少,且能得到更好的结果.     

散乱分布数据曲面重构的光顺-有限元方法

提出了一种基于散乱分布的数据点重构三维曲面的有限元方法.根据最佳逼近与数据光顺理论建立正定的目标泛函,采用有限元最佳拟合使泛函极小化,求得最优解.通过八节点等参数有限元插值计算,重新构造出三维曲面.这种光顺-有限元方法有效地抑制了输入数据上误差噪声的影响,与有限元拟合方法相比,所需的输入数据点少,重构的曲面逼近精度高、光顺性好.数值实验表明,该方法简单,便于应用.     

B样条曲面在严格约束状态下的光顺拟合

提出一种新的B样条曲面造型方法:光顺地拟合空间型值点且同时严格地通过其中部分点.运用Lagrange乘子的条件极值法并引入光顺加权项，求出位置偏离和形状弯曲的最小二乘解，可以得到被称为B样条光顺准拟合的良好造型曲面.这一方法在包含门窗的交通工具外壳曲面设计、机械产品装配联接件制造中具有明显的实用价值.     

壳空间剖分的隐式曲面三角化

提出了一种曲率自适应的壳空间剖分隐式曲面三角形化新方法.新方法首先采用粒子系统对隐式曲面进行采样,通过高斯曲率约束粒子的生成,使生成的网格模型在曲率大的区域具有较多的小三角形,在曲率小的区域具有较少的大三角形,从而使网格模型更好地逼近隐式曲面.新方法在每个采样粒子处沿曲面法线正负方向延伸适当距离得到两个附加点,对所有附加点进行四面体化形成对隐式曲面逼近的壳空间四面体网格,在每个壳空间四面体中抽取三角形,所有抽取的三角形拼合得到隐式曲面的三角网格表示.与以往方法相比,新的三角网格化方法更具有鲁棒性,可一次性获得高质量的三角形网格.最后给出了对常用隐式曲面进行三角化的实例比较,显示了新方法的有效性.     

基于能量最小化的网格优化算法

对网格优化的方法进行研究,提出一种基于能量最小化的网格优化算法.给定一定数量的三维散乱点数据和一个初始三角网格,使用能量最小化算法对网格顶点位置进行优化,使网格顶点更好地逼近三维散乱点数据;网格也更加逼近实际曲面.实验结果表明,使用该算法能够获得形状良好的网格.     

基于邻域相似性的三角网格光顺算法

为了提高三角网格模型的质量,满足模型后续处理的要求,提出了一种基于邻域相似性的网格光顺算法。类比图像像素的灰度值,首先构造顶点的双边滤波微分算子,作为其几何灰度值;然后计算顶点邻域之间的相似性,作为顶点几何灰度值的权值,并对顶点的邻域顶点的几何灰度值进行加权平均,得到该顶点的最终几何灰度值;最后将顶点沿着其法矢量方向移动几何灰度值大小的距离,得到光顺后的三角网格模型。实验证明,该算法在光顺模型的同时有效地保持了网格的几何特征。     

网格曲面近似“最直路径”的快速计算

为实现网格模型两点间最短路径的近似逼近,提出一种网格曲面上近似"最直路径"的计算方法.利用起点与终点邻域特征获得初始切割面,对网格曲面上的三角片逐步切割求交,扩展"最直路径";在求交过程中逐步调整切割平面的方向,直至终点.实验结果表明,文中方法快速、有效.     

Surface interpolation of meshes by geometric subdivision

Subdivision surfaces are generated by repeated approximation or interpolation from initial control meshes. In this paper, two new non-linear subdivision schemes, face based subdivision scheme and normal based subdivision scheme, are introduced for surface interpolation of triangular meshes. With a given coarse mesh more and more details will be added to the surface when the triangles have been split and refined. Because every intermediate mesh is a piecewise linear approximation to the final surface, the first type of subdivision scheme computes each new vertex as the solution to a least square fitting problem of selected old vertices and their neighboring triangles. Consequently, sharp features as well as smooth regions are generated automatically. For the second type of subdivision, the displacement for every new vertex is computed as a combination of normals at old vertices. By computing the vertex normals adaptively, the limit surface is G¹ smooth. The fairness of the interpolating surface can be improved further by using the neighboring faces. Because the new vertices by either of these two schemes depend on the local geometry, but not the vertex valences, the interpolating surface inherits the shape of the initial control mesh more fairly and naturally. Several examples are also presented to show the efficiency of the new algorithms.     

Quality Mesh Smoothing via Local Surface Fitting and Optimum Projection

The smoothness and angle quality of a surface mesh are two important indicators of the "goodness" of the mesh for downstream applications such as visualization and numerical simulation. We present in this paper a novel surface mesh processing method not only to reduce mesh noise but to improve angle quality as well. Our approach is based on the local surface fitting around each vertex using the least square minimization technique. The new position of the vertex is obtained by finding the maximum inscribed circle (MIC) of the surrounding polygon and projecting the circle's center onto the analytically fitted surface. The procedure above repeats until the maximal vertex displacement is less than a pre-defined threshold. The mesh smoothness is improved by a combined idea of surface fitting and projection, while the angle quality is achieved by utilizing the MIC-based projection scheme. Results on a variety of geometric mesh models have demonstrated the effectiveness of our method.     

基于支撑域的网格简化算法

提出一种基于2次误差测度（QEM）的网格简化改进算法。算法首先对折叠边所产生的新顶点定义其在初始网格上的简化支撑域,从而建立新顶点与初始网格之间的联系;然后计算新顶点到支撑域的2次距离误差作为该顶点的全局简化误差,并将原始QEM中的误差作为局部简化误差;最后将两个误差之和作为新的折叠代价目标函数以实现对原有QEM算法的改进。多个模型的简化实验表明,改进算法能较好地保留初始网格的细节特征,并且较为明显地降低简化误差。     

基于整数小波变换的三维网格数字水印算法

提出一种整数小波变换的三维网格数字水印算法.网格顶点到网格中心的距离具有全局几何特性,利用整数小波变换把距离序列变化到频域;然后在频域信号上嵌入水印信息,再通过逆整数小波变换转回到空域信号;最后根据新的空域信号的值修改网格上点的坐标得到加入数字水印的网格.实验结果表明:该算法实现简单,在简化攻击、噪音攻击情况下具有良好的鲁棒性.     

一种有效的细分曲面非盲水印算法*

提出的细分曲面水印算法是基于图像水印算法和Fourier加法性质。首先将水印信息嵌入一幅图像,通过嵌入水印的图像和原始图像在空域作比较得到含有水印信息的矩阵,然后将细分曲面初始网格迭代三次以使网格的顶点数足够多,最后取出一部分顶点坐标和含有水印信息的矩阵相加得到嵌入水印的网格,嵌入水印后的网格作为新的初始网格。实验结果证明该算法具有较好的强壮性和计算量小的特点。     

A new vector field distance transform and its application to mesh processing from 3D scanned data

In this paper we define a new 3D vector field distance transform to implicitly represent a mesh surface. We show that this new representation is more accurate than the classic scalar field distance transform by comparing both representations with an error metric evaluation. The widely used marching cube triangulation algorithm is adapted to the new vector field distance transform to correctly reconstruct the resulting explicit surface. In the reconstruction process of 3D scanned data, the useful mesh denoising operation is extended to the new vector field representation, which enables adaptive and selective filtering features. Results show that mesh processing with this new vector field representation is more accurate than with the scalar field distance transform and that it outperforms previous mesh filtering algorithms. Future work is discussed to extend this new vector field representation to other mesh useful operations and applications.     

Noise analysis and synthesis for 3D laser depth scanners

This paper analyses the noise present in range data measured by a Konica Minolta Vivid 910 scanner, in order to better characterise real scanner noise. Methods for denoising 3D mesh data have often assumed the noise to be Gaussian, and independently distributed at each mesh point. We show via measurements of an accurately machined almost planar test surface that real scanner data does not have such properties: the errors are not quite Gaussian, and more importantly, exhibit significant short range correlation. We use this to give a simple model for generating noise with similar characteristics. We also consider how noise varies with such factors as laser intensity, orientation of the surface, and distance from the scanner. Finally, we evaluate the performance of three typical mesh denoising algorithms using real and synthetic test data, and suggest that new denoising algorithms are required for effective removal of real noise.     

Fast and effective feature-preserving mesh denoising

We present a simple and fast mesh denoising method, which can remove noise effectively, while preserving mesh features such as sharp edges and corners. The method consists of two stages. Firstly, noisy face normals are filtered iteratively by weighted averaging of neighboring face normals. Secondly, vertex positions are iteratively updated to agree with the denoised face normals. The weight function used during normal filtering is much simpler than that used in previous similar approaches, being simply a trimmed quadratic. This makes the algorithm both fast and simple to implement. Vertex position updating is based on the integration of surface normals using a least-squares error criterion. Like previous algorithms, we solve the least-squares problem by gradient descent, but whereas previous methods needed user input to determine the iteration step size, we determine it automatically. In addition, we prove the convergence of the vertex position updating approach. Analysis and experiments show the advantages of our proposed method over various earlier surface denoising methods.     

A cascaded approach for feature-preserving surface mesh denoising

Mesh denoising is crucial for improving noisy meshes acquired from scanning devices and digitization processes. This paper proposes a general, robust approach for mesh denoising by using a combination of bilateral filtering, feature recognition, anisotropic neighborhood searching, and surface fitting and projection techniques. Motivated by the bilateral filtering from image processing applications, we develop a new bilateral filter operating on the normal vector fields of the mesh. Then, we detect mesh features and classify mesh vertices into non-feature vertices and feature vertices. The corresponding anisotropic neighborhoods for each vertex are searched by constructing a weighted dual graph, over which biquadratic Bezier surface patches are fitted and projected. The projection points are averaged to update each vertex of the mesh. The steps above are repeated iteratively until convergence, i.e., the Hausdorff distance between two sequential denoised meshes is less than a pre-defined threshold. A number of examples presented in the paper demonstrate that our method generally yields visually and numerically better denoising results, compared with the state-of-the-art methods.     

Mesh filtering algorithm using an adaptive 3D convolution kernel applied to a volume-based vector distance field

This paper addresses the problem of feature preserving mesh filtering, which occurs in surface reconstruction of scanned objects, which include acquisition noise to be removed without altering sharp edges. We propose a method based on a vector field distance transform of the mesh to process. It is a volume-based implicit surface modeling, which provides an alternative representation of meshes. We use an adaptive 3D convolution kernel applied to the voxels of the distance transform model. Weights of the kernel elements are determined according to the angle between the vectors of the implicit field. We also propose a new adaptation of the Marching Cubes algorithm in order to extract the isosurface from the vector implicit field after the filtering process. We compare our method to the previous one introduced using the vector field representation and to other feature preserving adaptive filtering algorithms. According to error metric evaluations, we show that our new design provides high quality filtering results while better preserving geometric features.