隐式T样条实现封闭曲面重建

为了简化法向偏差约束条件和优化光滑能量项,提出一种隐式T样条曲面重建算法.首先利用八叉树及其细分过程从采样点集构造三维T网格,以确定每个控制系数对应的混合函数;然后基于隐式T样条曲面建立目标函数,利用偏移曲面点集控制法向,采用广义交叉检验(GCV)方法估计最优光滑项系数,并依据最优化原理将该问题转化为线性方程组求解得到控制系数,从而实现三角网格曲面到光滑曲面的重建.在误差较大的区域插入控制系数进行T网格局部修正,使得重建曲面达到指定精度.该算法使重建曲面C1连续条件得到松弛,同时给出最优的光顺项系数估计,较好地解决了封闭曲面的重建问题.实例结果表明,文中算法逼近精度高,运算速度快,仿真结果逼真.     

有理B样条曲面的区间隐式化

提出有理B样条曲面的区间隐式化方法,即对一个有理B样条曲面,寻求包含给定的曲面的区间隐式B样条曲面,使得区间隐式B样条曲面的"厚度"尽量小,同时尽量避免出现多余分支.该问题等价于求区间隐式B样条曲面的2个边界曲面.针对该问题建立一个最优化模型并求解.     

自适应T样条曲面重建

为了进行快速高精度的曲面重建,提出了一种新的基于T样条的曲面自动重建算法。由于T样条控制网格具有特殊性质,因此在使用T样条进行曲面重建时,一个关键的问题是如何构造好一个T网格。该新算法在进行曲面重建时,用三角网格的参数化方法,先将数据点同胚映射到平面,然后再利用平面四叉树细分的方法将无结构散乱数据自动生成合理有效的T网格,最后将曲面重构模型转化为最优化问题,并由最小二乘法求解,同时在误差较大的区域辅以T样条的局部修正,以使重建曲面与原网格面的最大误差小于指定的误差值。由于该新的曲面重建方法是一个基于细节的重建方法,因此采样点密集区域所插入的T网格点也就相应地增多,这样既抓住了网格曲面的特征,又能很好地减少过多的T网格控制顶点,这就提高了算法效率。另外,该新算法还具有高效、易操作、能适应复杂曲面重建、曲面自动生成且满足相应精度要求等优点。重构结果显示,该新的曲面重建算法不仅重构应用范围广,且重构精度高。     

T样条曲面在B样条曲面局部拼接中的应用

为解决相邻B样条曲面在局部拼接时由于曲面细分而加入冗余控制点的问题,利用T样条曲面良好的局部细分性,提出一种更有效的T样条曲面局部拼接法。将相邻B样条曲面精确转换为两张全行列T样条曲面;利用T样条曲面的局部均匀细分算法得到局部拼接边界上的一致性,在曲面上加入局部控制点;将局部细分后的曲面连续拼接为一张控制点更少的T样条组合曲面。与B样条曲面的细分拼接法相比,该方法在曲面细分时不加入任何冗余控制点,在局部拼接后能够得到一张更精简的组合曲面。     

径向基函数网络的隐式曲面方法

将径向基函数网络与隐式曲面构造原理相结合，提出一种构造隐式曲面的方法．首先以描述物体曲面的隐式函数为基础构造三元显式函数，然后用径向基函数网络逼近显式函数，最后从神经网络的仿真超曲面得到描述物体的封闭曲面；并证明了在理论上此等值面可以以任意精度逼近物体曲面．该方法具有光滑度高、稳定性好，尤其适用少量采样点情形等特点．实验表明，它具有很强的造型能力．     

层次隐式张量积B-样条曲面及其在曲面重构中的应用

提出一类新的隐式曲面表示形式,它具有良好的层次性与自适应性,特别适合于表示层次细节模型.首先给出层次隐式张量积B-样条曲面的定义,回顾曲面重构的一般数学模型,然后提出适于求解该类曲面的最优化模型以及定义域自适应分解的方法.在此基础之上,提出层次逼近算法来逼近采样点数据集.随后,引入单位分解方法,将其用于融合各个子域上的局部逼近函数,使其成为一个具有整体光滑性的函数.最后基于散乱点数据集,给出曲面重构的实例,并作简单的讨论.     

散乱点云数据的高阶平滑隐式曲面重建

针对三维扫描或三维重建获取的散乱点云数据曲面重建问题, 提出基于拉普拉斯规则化的高阶平滑算法。首先, 计算点云数据的包围盒并离散化得到体素空间; 其次, 在体素空间根据隐式曲面的梯度和点云位置、法向信息建立目标函数, 并通过对目标函数的拉普拉斯规则化达到控制重建曲面光顺效果的目的; 再次, 根据最优化原理将重建问题转换为一个稀疏线性方程组求解问题; 最后, 通过步进立方体算法得到重建曲面的三角网格表示。定性和定量的实验结果表明, 该方法重建曲面绘制效果和精确度优于常用的Poisson方法。     

隐式曲面的快速适应性多边形化算法

通过将隐式曲面多边形化过程分为“构造”和“适应性采样”两个阶段，实现了隐式曲面多边形逼近网格的适应性构造．通过基于空间延展的Marching Cubes方法得到隐式曲面较为粗糙的均匀多边形化逼近，根据曲面上的局部曲率分布，运用适应性细分规则对粗糙网格进行细分迭代，并利用梯度下降法将细分出的新顶点定位到隐式曲面上；最终得到的多边形网格是适应性的单纯复形网格，其在保持规定逼近精度的前提下，减少了冗余三角形的产生，网格质量有明显改善．该算法可用于隐式曲面的交互式可视化过程．     

基于最佳圆弧样条逼近的快速距离曲面计算

距离曲面是一种常用的隐式曲面,它在几何造型和计算机动画中具有重要的应用价值,但以往往在对距离曲面进行多边形化时速较慢,为了提高点到曲线最近距离计算的效率,提出了一种基于最佳圆弧样条逼近的快速线骨架距离曲面计算方法,该算法对于一条任意的二维NURBS曲线,在用户给定的误差范围内,先用最少量的圆弧样条来逼近给定的曲线,从而把点到NURBS曲线最近距离的计算问题转化为点到圆弧样条最近距离的计算问题,由于在对曲面进行多边形化时,需要大量的点到曲线最近距离的计算,而该处可以将点到圆弧样条最近距离很少的计算量来解析求得,故该算法效率很高,该实验表明,算法简单实用,具有很大的应用价值。     

推广Dirichlet 方法用于B 样条极小曲面设计

为弥补当前NURBS系统无法有效设计工程所急需的B样条极小曲面的缺陷,将构造Bézier极小曲面的Dirichlet方法成功地推广到了B样条极小曲面设计.提出了插值控制网格边界的B样条曲面模型,运用B样条基函数的求导公式及求值割角算法,将计算极小曲面内部控制顶点的问题转化为一个线性方程组的求解,从而避免了强非线性问题所导致的困惑,极大地提高了运算效率.最后,用大量实例对理论和算法进行了验证.     

带法向约束的隐式T样条曲线重构

目的 隐式曲线能够描述复杂的几何形状和拓扑结构,而传统的隐式B样条曲线的控制网格需要大量多余的控制点满足拓扑约束。有些情况下,获取的数据点不仅包含坐标信息,还包含相应的法向约束条件。针对这个问题,提出了一种带法向约束的隐式T样条曲线重建算法。方法 结合曲率自适应地调整采样点的疏密,利用二叉树及其细分过程从散乱数据点集构造2维T网格;基于隐式T样条函数提出了一种有效的曲线拟合模型。通过加入偏移数据点和光滑项消除额外零水平集,同时加入法向项减小曲线的法向误差,并依据最优化原理将问题转化为线性方程组求解得到控制系数,从而实现隐式曲线的重构。在误差较大的区域进行T网格局部细分,提高重建隐式曲线的精度。结果 实验在3个数据集上与两种方法进行比较,实验结果表明,本文算法的法向误差显著减小,法向平均误差由10^-3数量级缩小为10^-4数量级,法向最大误差由10^-2数量级缩小为10^-3数量级。在重构曲线质量上,消除了额外零水平集。与隐式B样条控制网格相比,3个数据集的T网格的控制点数量只有B样条网格的55.88%、39.80%和47.06%。结论 本文算法能在保证数据点精度的前提下,有效降低法向误差,消除了额外的零水平集。与隐式B样条曲线相比,本文方法减少了控制系数的数量,提高了运算速度。     

3维图像中边界曲面的分类追踪及抽取

3维图像分析中,边界曲面的检测与重构是一个非常重要的问题。已有的连续隐边界曲面的抽取及逼近计算技术存在着把某些零交叉曲面片错误地识别为边界曲面片的缺陷。为此,提出一个新的边界曲面的追踪及抽取的方法。该方法首先将包含边界曲面的全部立方体分为两类:包含一个连通零交叉曲面片的立方体叫第1类边缘立方体,包含两个及其以上不连通零交叉曲面片的立方体叫第2类边缘立方体;然后根据边界曲面的连续性连通性,便可追踪出两类边缘立方体;对于追踪出的第1类边缘立方体直接提取边界曲面片,对于追踪出的第2类边缘立方体的边界曲面片通过其相邻的第1类边缘立方体来提取。实验结果表明本文方法是可行有效的,而且可以有效地克服已有技术的缺陷。     

Evolution of T-spline level sets for meshing non-uniformly sampled and incomplete data

Given a large set of unorganized point sample data, we propose a new framework for computing a triangular mesh representing an approximating piecewise smooth surface. The data may be non-uniformly distributed, noisy, and may contain holes. This framework is based on the combination of two types of surface representations, triangular meshes and T-spline level sets, which are implicit surfaces defined by refinable spline functions allowing T-junctions. Our method contains three main steps. Firstly, we construct an implicit representation of a smooth (C ² in our case) surface, by using an evolution process of T-spline level sets, such that the implicit surface captures the topology and outline of the object to be reconstructed. The initial mesh with high quality is obtained through the marching triangulation of the implicit surface. Secondly, we project each data point to the initial mesh, and get a scalar displacement field. Detailed features will be captured by the displaced mesh. Finally, we present an additional evolution process, which combines data-driven velocities and feature-preserving bilateral filters, in order to reproduce sharp features. We also show that various shape constraints, such as distance field constraints, range constraints and volume constraints can be naturally added to our framework, which is helpful to obtain a desired reconstruction result, especially when the given data contains noise and inaccuracies.     

Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions

Fast high-precision patient-specific vascular tissue and geometric structure reconstruction is an essential task for vascular tissue engineering and computer-aided minimally invasive vascular disease diagnosis and surgery. In this paper, we present an effective vascular geometry reconstruction technique by representing a highly complicated geometric structure of a vascular system as an implicit function. By implicit geometric modelling, we are able to reduce the complexity and level of difficulty of this geometric reconstruction task and turn it into a parallel process of reconstructing a set of simple short tubular-like vascular sections, thanks to the easy-blending nature of implicit geometries on combining implicitly modelled geometric forms. The basic idea behind our technique is to consider this extremely difficult task as a process of team exploration of an unknown environment like a cave. Based on this idea, we developed a parallel vascular modelling technique, called Skeleton Marching, for fast vascular geometric reconstruction. With the proposed technique, we first extract the vascular skeleton system from a given volumetric medical image. A set of sub-regions of a volumetric image containing a vascular segment is then identified by marching along the extracted skeleton tree. A localised segmentation method is then applied to each of these sub-image blocks to extract a point cloud from the surface of the short simple blood vessel segment contained in the image block. These small point clouds are then fitted with a set of implicit surfaces in a parallel manner. A high-precision geometric vascular tree is then reconstructed by blending together these simple tubular-shaped implicit surfaces using the shape-preserving blending operations. Experimental results show the time required for reconstructing a vascular system can be greatly reduced by the proposed parallel technique.

     

Compact implicit surface reconstruction via low-rank tensor approximation

Implicit representations have gained an increasing popularity in geometric modeling and computer graphics due to their ability to represent shapes with complicated geometry and topology. However, the storage requirement, e.g. memory or disk usage, for implicit representations of complex models is relatively large. In this paper, we propose a compact representation for multilevel rational algebraic spline (MRAS) surfaces using low-rank tensor approximation technique, and exploit its applications in surface reconstruction. Given a set of 3D points equipped with oriented normals, we first fit them with an algebraic spline surface defined on a box that bounds the point cloud. We split the bounding box into eight sub-cells if the fitting error is greater than a given threshold. Then for each sub-cell over which the fitting error is greater than the threshold, an offset function represented by an algebraic spline function of low rank is computed by locally solving a convex optimization problem. An algorithm is presented to solve the optimization problem based on the alternating direction method of multipliers (ADMM) and the CANDECOMP/PARAFAC (CP) decomposition of tensors. The procedure is recursively performed until a certain accuracy is achieved. To ensure the global continuity of the MRAS surface, quadratic B-spline weight functions are used to blend the offset functions. Numerous experiments show that our approach can greatly reduce the storage of the reconstructed implicit surface while preserve the fitting accuracy compared with the state-of-the-art methods. Furthermore, our method has good adaptability and is able to produce reconstruction results with high quality.     

基于BP神经网络的隐式曲面构造方法

通过把BP神经网络与隐式曲面构造原理相结合,提出构造隐式曲面的新方法.用约束点来描述、控制曲面形状,构造BP网的输入与输出,通过智能学习、仿真模拟,最后从仿真超曲面抽取出的零等值面就是隐式曲面.同时,从理论上证明了此方法所构造的隐式曲面具有任意精度.实验表明该方法对约束点的个数、误差、内外点与边点的距离等不敏感,表现出很好的稳定性与可操作性.该构造方法不仅可用于构造隐式曲面,而且在图形理解、数据分类等领域也具有良好的应用前景.     

基于任意骨架的隐式曲面造型技术

给出了一个新的基于任意多面体网格骨架的构造性自由曲面造型算法.算法首先由每个给定骨架构造出一个距离场,然后利用隐函数光滑过渡技术和CSG(constructive solid geometry)表示技术将所构造的隐式曲面自由地两两粘合成一张光滑曲面.隐式曲面的多边形化算法则用来生成最终曲面网格.以任意骨架作为基本体素,突破了传统隐式曲面以点为基本骨架的限制.而且,距离曲面很好地逼近了原骨架形状,使用户可直观地对复杂曲面进行交互设计.而形变函数的引入,则极大地丰富了此方法的造型能力.实验结果表明,基于该算法的原型系统能够方便、直观地构造复杂的自由曲面.