散乱点的快速曲面重建方法

空间散乱点的曲面重建有着广泛的应用前景，是当前国际上的研究热点之一，Crust算法是一种基于计算几何中的Voronoi周期图的曲面重建算法，它算法简单，重建结果精细，但是由于计算量太大，其应用受到了限制，为此提出了一种依据采样点的局部特征尺度对原始采样集进行不均匀降采样的方法，在保证采样集能够满足重建要求的前提下，使参与重建的表面点数大为降低，减少了重建算法的计算量，从而提高了重建的速度，这一方法还可以应用于网络简化，通过剔除某些顶点达到简化之目的。     

面向非均匀采样点集的3维表面重建算法

针对非均匀采样点集,提出一种改进的3维表面重建方法。该方法将整个点集进行空间划分,缩小近邻点的搜索范围,减少搜索时间;在确定近邻点时,先计算几何近邻点,然后通过求方向性点并构造最小生成树的方法,确定拓扑近邻点;最后通过将拓扑近邻点投影到局部切平面上,利用约束条件对投影点进行三角剖分,并将剖分得到的顶点连接关系映射到3维空间中,实现3维表面重建。实验结果表明,改进后的算法运行效率高、重建效果好、广泛适用于非均匀采样点集的表面重建。     

基于边界检测的三维散乱点快速曲面重建算法

针对Power Crust算法提出一种带边界检测的不均匀降采样算法。曲面重建前先通过该算法减少参与运算的采样点,表面特征丰富的区域削减的采样点数远小于特征不丰富的区域,再进行曲面重建。通过实例表明该算法大大加快了散乱点数据的重建速度,而且很好地保持了模型表面的特征,能够较为真实地重建出曲面模型。     

Automatic reconstruction of B-spline surfaces with constrained boundaries

The aim of this study is to present an automatic surface reconstruction method that can take practical restrictions on scanned points into consideration and efficiently and reliably output a group of G¹ surfaces. The proposed method is mainly composed of three phases: quadrangle frame generation, point and curve networks planning, and surface patches reconstruction. In the first phase, the original triangle mesh is reduced and converted into a quadrangle mesh, the edges of which serve as the frame of the surfaces. In the second phase, the boundary data of the surfaces are prepared. These include a network of serial points, frame curves and surface normals which are also expressed as curves. In the final phase, surface initialization, harmonization mapping and surface warping are presented to yield the desired surfaces. The main advantage of the proposed method is that it can relax the pre-processing of a scanned triangle mesh, and hence, increase the efficiency and quality of the surface reconstruction. Several examples of various types of air bags are presented to demonstrate the feasibility of the proposed method.     

基于标志点的虚拟剖切面重建

针对现有医学图像技术中三维模型的虚拟剖切面重建算法功能单一、不能完成复杂路径剖切面展开的缺点,提出基于空间任意标志点的剖切面重建算法。该算法可以沿三维空间中的任意路径完成虚拟剖切面展开,包括确定标志点、拟合虚拟剖切路径、重建剖切面3个部分。实验证明该算法可以得到稳定优异的重建图像,精确度较高。     

Distributed Poisson Surface Reconstruction

Screened Poisson surface reconstruction robustly creates meshes from oriented point sets. For large datasets, the technique requires hours of computation and significant memory. We present a method to parallelize and distribute this computation over multiple commodity client nodes. The method partitions space on one axis into adaptively sized slabs containing balanced subsets of points. Because the Poisson formulation involves a global system, the challenge is to maintain seamless consistency at the slab boundaries and obtain a reconstruction that is indistinguishable from the serial result. To this end, we express the reconstructed indicator function as a sum of a low-resolution term computed on a server and high-resolution terms computed on distributed clients. Using a client–server architecture, we map the computation onto a sequence of serial server tasks and parallel client tasks, separated by synchronization barriers. This architecture also enables low-memory evaluation on a single computer, albeit without speedup. We demonstrate a 700 million vertex reconstruction of the billion point David statue scan in less than 20 min on a 65-node cluster with a maximum memory usage of 45 GB/node, or in 14 h on a single node.     

基于Delaunay三角化的噪声点云非均匀采样

针对基于Delaunay三角化曲面重建方法要求点云密度满足ε-sample条件,提出了一种基于Delaunay三角化的噪声点云非均匀采样算法。首先,利用k-邻近点的Voronoi顶点计算出各点的负极点来逼近曲面中轴(MA);然后,根据近似中轴估计出曲面局部特征尺度(LFS);最后,结合Bound Cocone算法,删除多余的非边界点。实例表明,该算法可以准确、稳健地简化噪声点云,同时可以很好地保留曲面边界特征,经简化后的点云适用于基于Delaunay三角化的曲面重建方法。     

Reconstructing Curves without Delaunay Computation

A new non-Delaunay-based approach is presented to reconstruct a curve, lying in 2- or 3-space, from a sampling of points. The underlying theory is based on bounding curvature to determine monotone pieces of the curve. Theoretical guarantees are established. The implemented algorithm, based heuristically on the theory, proceeds by iteratively partitioning the sample points using an octree data structure. The strengths of the approach are (a) simple implementation, (b) efficiency-experimental performance compares favorably with Delaunay-based algorithms, (c) robustness-curves with multiple components and sharp corners are reconstructed satisfactorily, and (d) potential extension to surface reconstruction.     

一种改进的区域扩张三角网格面逆向重构算法

三角网格面重构是逆向工程中常用的曲面重构方法之一．本文提出了一种基于局部信息的区域扩张算法（LIBRG），用以解决由离散点云重构三角网格面的问题．该算法由一个初始三角片开始，扩张式地搜索满足要求的点以生成新的三角片．算法充分利用了区域扩张过程中的局部空间信息，所以能很好地适应曲率的变化并自动筛选掉一些无效点；同时LIBRG算法也针对传统算法中的全程遍历搜索方式进行了改进，以获得较高的时间效率．     

基于隐式T样条的曲面重构算法

提出隐式T样条曲面，将T网格从二维推广到三维情形，同时利用八叉树及其细分过程，从无结构散乱点数据集构造T网格，利用曲面拟合模型将曲面重构问题转化为最优化问题；然后基于隐式T样条曲面将最优化问题通过矩阵形式表述，依据最优化原理将该问题转化成线性方程组，通过求解线性方程组解决曲面重构问题；最后结合计算实例进行讨论．该方法能较好地解决曲面重构问题，与传统张量B样条函数相比，能效地减少未知控制系数与计算量．     

Shape classification and normal estimation for non-uniformly sampled, noisy point data

We present an algorithm for robustly analyzing point data arising from sampling a 2D surface embedded in 3D, even in the presence of noise and non-uniform sampling. The algorithm outputs, for each data point, a surface normal, a local surface approximation in the form of a one-ring, the local shape (flat, ridge, bowl, saddle, sharp edge, corner, boundary), the feature size, and a confidence value that can be used to determine areas where the sampling is poor or not surface-like.We show that the normal estimation out-performs traditional fitting approaches, especially when the data points are non-uniformly sampled and in areas of high curvature. We demonstrate surface reconstruction, parameterization, and smoothing using the one-ring neighborhood at each point as an approximation of the full mesh structure.     

切片级管线表面三维重建

在比较表面重建中的面绘制法和体绘制法以及面绘制中的切片级和体素级两种表面重建方法、分析表面重建流程的基础上,提出了一种计算型值点集的方法,它在计算弯道部分的管线时较为简便。该方法通过二次曲面求管线表面轮廓方程进而求得管线的型值点集。之后用三角网格对型值点集进行拼接来获得管线的表面模型,最后利用图形学的方法对管线表面模型进行绘制,达到管线表面三维重建的目的。文末利用Java3D给出了一个具体的实现。     

CT投影采样策略对重建质量影响综述

针对计算机断层成像（Computed Tomography,CT）中投影数据与图像重建关系,综述了CT在投影策略方面对重建质量的影响.对不同采样策略获取的不完全投影数据,应用迭代类算法对投影数据进行重建,研究了均匀采样和非均匀采样情况下不同数据结构对重建图像质量的影响.对仿真数据和实际数据重建结果进行分析,同时对不同策略下的投影数据结合其数据分布特点探讨了重建质量优劣的原因.论文可以为CT重建领域的研究工作者提供全面的采样方法梳理和总结,为当前不完全投影数据获取方式对应的算法改进提供思路,最后对当前研究重点和未来发展进行了展望.     

三维重建中散乱点云的聚类筛选与网格重建

三维重建过程中获得的初始海量数据存在大量的噪声和孤立点,使得直接使用这些数据进行网格重建时,将会产生尖锐的凸出,导致重建效果不好,甚至是网格重建失败.针对以上问题,提出首先采用基于密度聚类的方法筛选三维点云,然后进行网格重建.实验表明本文算法获得了较好的网格重建效果.     

相干采样在混合信号模块校准中的应用

文章介绍了混合信号测试中采样和重构的原理,针对相干采样技术进行了深入的研究,分析了采样周期、采样点、相干频率和采样频率的关系。同时以ASL3000集成电路测试系统为背景,利用相干采样理论建立、产生重构信号,实现了对集成电路测试系统任意波形发生器的精确校准。校准结果表明,该校准装置及校准方法能够满足集成电路测试系统任意波形发生器的校准要求。     

Continuous global optimization in surface reconstruction from an oriented point cloud

We introduce a continuous global optimization method to the field of surface reconstruction from discrete noisy cloud of points with weak information on orientation. The proposed method uses an energy functional combining flux-based data-fit measures and a regularization term. A continuous convex relaxation scheme assures the global minima of the geometric surface functional. The reconstructed surface is implicitly represented by the binary segmentation of vertices of a 3D uniform grid and a triangulated surface can be obtained by extracting an appropriate isosurface. Unlike the discrete graph-cut solution, the continuous global optimization entails advantages like memory requirements, reduction of metrication errors for geometric quantities, and allowing globally optimal surface reconstruction at higher grid resolutions. We demonstrate the performance of the proposed method on several oriented point clouds captured by laser scanners. Experimental results confirm that our approach is robust to noise, large holes and non-uniform sampling density under the condition of very coarse orientation information.     

平面简单闭合曲线离散采样与重建算法

提出一种鲁棒的平面简单闭合曲线离散采样与重建算法。算法分为采样过程和重 建过程两部分。采样部分首先对平面闭合曲线均匀取点,然后计算各点到曲线所围平面区域中 轴的最近距离,最后根据所求距离确定采样间隔,获取采样点集;重建部分首先构建采样点集 的Delaunay 三角剖分,然后从得到的三角形中选择边构建初始化图形,最后通过修改该图形获 得重建图形。实验表明算法得到的采样点较少且能反映曲线的局部几何特性,重建图形能够较 好地表示原闭合曲线的形状及走向。     

激光三维扫描数据的表面重建

对激光三维扫描系统获得的没有任何附加信息的轮廓线点云数据进行处理,首先采用求最大连通域的方法删除噪声点,利用设定相邻点连线夹角正切阈值的方法精简数据,然后采用基于局部切平面簇的方法对数据点云进行切平面的估算、法向量的调整和计算距离函数,用改进的MC方法输出三维网格,并且应用基于顶点的网格删除算法对三维网格进行简化,在估算切平面的时候采用新的估算原则,提高了重建速度,改善了重建效果,所表述的重建流程,成功地解决了激光扫描系统所得轮廓数据点的表面重建问题。     

基于局部曲面拟合的散乱点云简化方法

随着数据获取手段的进步,散乱点云数据在三维重建中获得越来越广泛的应用,然而庞大的数据量往往影响重建的效率。现有简化算法中采用的曲率计算方法精度不高,导致模型特征模糊。本文在分析曲面特征的基础上给出了一种曲面特征的定量描述方法。该方法采用局部曲面拟合得到曲面在一点处的近似曲面,然后用法曲率在360度范围内的平均值代替平均曲率来描述曲面在一点处的特征。简化时采用K-D树剖分点云数据,根据子节点所包含的采样点数、空间区域大小和曲面特征大小控制简化过程。实验结果表明,该方法能够更好地保持曲面的几何特征,从而证明了算法的有效性。     

基于改进全卷积神经网络的医疗数据表面重建算法

为了实现对医疗数据的快速检测和分类识别,需要对医疗数据进行表面重建设计,首先,提出一种基于改进全卷积神经网络的医疗数据表面重建算法.采用无线射频识别技术进行医疗数据的大数据采样,对RFID采集的医疗数据进行信息融合处理,采用多元回归分析方法提取医疗数据的相关性统计特征量,然后,针对医疗数据中的冗余特征采用匹配滤波检测器进行冗余滤波处理,对提纯后的医疗数据采用相空间重构技术实现医疗数据重构,最后,对重构数据采用改进全卷积神经网络分类器进行分类识别,实现医疗数据的表面重建和自动识别.仿真结果表明,所提方法的医疗数据冗余特征处理效果较好,数据分类精度可高达90%以上,且医疗数据重建误差小,耗时少.