基于前向查找和均值漂移的点模型鲁棒降噪算法

针对点模型提出了基于前向查找和均值漂移两种鲁棒统计方法的滤波算法。前向查找算法根据残差图自动检测离群点，并将输入的点云数据划分为多个不带离群点的最优局部降噪邻域。对局部邻域进行加权协方差分析，估计出该邻域的最小二乘拟合平面。在局部邻域内估计采样点的核密度函数并通过均值漂移算法计算它的局部最大值点，核密度函数的局部最大值点确定了点云数据的聚类中心并能准确逼近采样点曲面，将每一个采样点漂移到密度函数的局部最大值点，使点云曲面收敛为一个稳定的三维数字模型。实验结果表明，本文的算法是鲁棒的，能在有效剔除点模型表面噪声的同时较好地保持模型表面的尖锐特征。     

点云数据集的隐式曲面重构研究进展

点云数据的曲面重建就是对扫描设备获得的物体散乱数据点重建三维物体表面,它被广泛应用于计算机动画、目标识别、数据可视化以及地理信息系统。点云的隐式曲面重建由于能够去除点云噪声,修补孔洞和裂缝,不需要拼接和平滑等后续处理,成为点云数据集曲面重构的重要方法。文中综述了目前一些主要的隐式曲面重构方法,就隐式模型以及相应的曲面重构算法的优缺点进行了分析比较,并对隐式曲面重构存在的问题和未来发展方向作了相应的分析和讨论。     

视觉传感器应用中三维扫描点云数据处理的研究

分析了便携式激光视觉扫描系统获取的点云数据存在的问题,针对具体问题分析了数据处理中的关键步骤和算法,使用手动剔除和系统判断相结合的方法,有效地剔除扫描数据中的噪声数据。同时,采用数据缩减算法实现对扫描点云的采样,在保证扫描曲面特征不失真的情况下,尽可能地缩减不必要的数据。数据经过处理后,不仅可以提高模型重构的精准度,更可以降低模型重构的复杂程度。     

基于高斯似然估计因子分析的点云配准算法

为解决三维点云数据在白噪声、数据不对应的情况下的配准问题,提出基于高斯似然估计因子分析的点云配准算法。采用因子分析法对点云数据进行表示,利用极大似然估计的方法求得因子载荷矩阵,从而完成对带噪声点云的配准。仿真实验表明,与CDP算法和Go-ICP算法相比,该算法不会陷入局部最小值,在快速精确配准和稳定性方面具有良好的鲁棒性。     

三维散乱点云模型的快速曲面重建算法

针对离散点云数据快速重构曲面的问题,提出一种基于紧支径向基函数(CSRBF)的2层隐式函数插值算法.首先在插值前设定一个中心减少阈值,以减少CSRBF的中心点,简化基于CSRBF的线性系统;然后在粗层上通过插值对点云模型进行逼近;再在细层上拟合曲面并对粗、细层曲面求和;最后引入一个正则化参数,将由CSRBF组成的矩阵正则化,处理携带噪声的三维点云模型.实验结果表明,该算法不仅能够简捷地处理含有噪声的三维点云数据,而且能够实现曲面快速重构,最终得到逼真、平滑的曲面模型.     

基于逆向工程的飞机曲面重建方法研究磁

介绍了逆向工程的基本知识,飞机曲面数据采集的一般流程;提出了点云数据预处理算法实现,包括点云滤波算法、点云数据精简算法和基于奇异值分解法的多视点云拼接算法,通过对数据的预处理,大部分数据噪声得以消除,数据量进一步简化,多视点云数据实现空间配准;研究了飞机曲面重建的数据处理流程,对飞机机体进行若干分区,按照点、线、面的建模处理流程对每个区进行独立建模;最后以Catia逆向建模模块对某型样机点云数据处理流程为例,详细探讨了飞机曲面重建的流程,精度验证等方法实现,实践证明,论文介绍的算法和处理方法切实有效,建模数据准确可靠。     

离散点云原始形状及边界曲线提取算法

大规模离散点云包含多种类型的扫描缺陷:噪声、异常数据、孔洞及不规则的各向异性采样,大部分现有的算法不能够很好地处理这些缺陷,这对点云拓扑关系的恢复及特征提取带来了困难.针对此问题,提出了一种健壮有效的点云重构算法,首先,计算每个数据点的局部属性;然后利用局部属性探测点云中包含的原始形状;最后利用统计优化方法对原始形状中...     

基于正交投影约束的点模型去噪

传统点云消噪算法会削弱曲面特征.为此,提出一种基于正交投影约束的点模型去噪算法.利用移动最小二乘曲面投影的思想,根据采样点与其在MLS曲面上正交投影点之间的关系构建移动距离权重函数,为防止模型收缩,给出曲率权重函数,通过双边滤波器确定滤波方向,结合移动距离权因子与曲率权因子确定采样点滤波距离.实验结果表明,该算法在消除点云噪声的同时,能保持点云高频结构特征,避免模型的收缩和顶点漂移.     

空间超限邻域点云去噪算法的研究与实现

对获取的点云数据进行降噪处理,是曲面重构过程的关键技术之一。充分利用三维空间散乱点的深度信息,重新诠释图像去噪中的＂超限邻域平均法＂,使其对二维图像的运用转换为对三维图形数据点的操作,并结合运用空间解析几何理论,提出一种直接对三维无组织散乱点去噪算法。从试验结果来看,本算法在去除噪声点的同时很好地保留了散乱点模型的细节特征,噪声去除效果理想。     

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。     

基于非局部几何信号的点模型去噪算法

与传统的基于局部几何信号去噪方法不同,提出了一种基于非局部几何信号的点模型去噪算法.该算法通过双边滤波算子,计算出每个点的微分坐标信息作为“几何灰度值”;基于模型上每个点的邻域的微分信息进行相似性匹配计算,对点模型上的“几何灰度值”进行全局加权平均,获得该点最终的微分信号;最后重建出该点的几何信息.进一步,提出了基于混合树的加速方法,对具有相似特征的邻域进行聚类,减少了匹配的空间复杂度,提高了计算效率.实验结果表明,算法简单高效,获得了满意的去噪效果.     

基于变形与测地距离一致性约束的3D面皮点对应

针对三维面皮生理点对应关系建立这一难题,充分考虑测地距离在描述复杂几何体表面形状方面的优势,提出了基于变形与测地距离一致性约束的3D面皮生理点对应方法。首先在Frankfurt坐标变换后标定面皮特征点集,利用特征点对应关系进行TPS变形;然后根据特征点几何特征向量建立初始点对应关系集,并利用测地距离一致性约束对其进行修剪以生成对应关系核心集;最后扩展对应关系核心集,直至确定源模型上每一顶点的对应关系。实验表明,该方法提高了点对应关系准确度,可有效建立三维面皮生理点对应关系。     

散乱点云数据曲率估计方法

针对带有强噪声离散点云数据曲率计算问题,提出一种基于稳健统计的曲率估计方法。首先,用一个二次曲面拟合三维空间采样点处的局部形状;其次,随机地选择该采样点邻域内的子集,多次执行这样的拟合过程,通过变窗宽的最大核密度估计,就得到了最优拟合曲面;最后,将采样点投影到该曲面上,计算投影点曲率信息,就得到采样点曲率。实验结果表明,所提方法对噪声和离群点是稳健的,特别是随着噪声方差的增大,要明显好于传统的抛物拟合方法。     

Multi-Level Partition of Unity Algebraic Point Set Surfaces

We present a multi-level partition of unity algebraic set surfaces (MPU-APSS) for surface reconstruction which can be represented by either a projection or in an implicit form. An algebraic point set surface (APSS) defines a smooth surface from a set of unorganized points using local moving least-squares (MLS) fitting of algebraic spheres. However, due to the local nature, APSS does not work well for geometry editing and modeling. Instead, our method builds an implicit approximation function for the scattered point set based on the partition of unity approach. By using an octree subdivision strategy, we first adaptively construct local algebraic spheres for the point set, and then apply weighting functions to blend together these local shape functions. Finally, we compute an error-controlled approximation of the signed distance function from the surface. In addition, we present an efficient projection operator which makes our representation suitable for point set filtering and dynamic point resampling. We demonstrate the effectiveness of our unified approach for both surface reconstruction and geometry modeling such as surface completion.     

Application of the Msplit method for filtering airborne laser scanning data-sets to estimate digital terrain models

ALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey conditions.

This article proposes the use of a new estimation method in the filtering of point clouds obtained from airborne laser scanning (ALS), provisionally called M_split-estimation. The application of M_split-estimation in ALS data filtering requires the determination of the appropriate functional model for the surface, which will be used in the filtering of the set of points. A polynomial terrain surface model was selected for this purpose. Two methods of filtering using the M_split method are presented. The first is based on the estimated parameters of the polynomial describing the surface (called the ‘quality’ approach in the article). The second method (provisionally called the ‘quantity’ method) is carried out in two stages. The first stage is point cloud filtering, which results in two subsets being created. One of these is the subset of points intended for DTM creation, while the other contains the remaining points. The second stage of the approach is the creation of a DTM from the first subset.

Since the M_split method has an analytical character, the ATIN method was selected to verify the correct operation of the method. The ATIN method is based on computational geometry and uses repeated Delaunay triangulation and statistical evaluation of the geometric parameters. Comparison of M_split with a method based on different principles mitigates errors arising from similarly functioning methods belonging to the same group of filters. The choice of the ATIN method was also dictated by its established position among filtering algorithms. The method is well-known, documented, and verified and this ensures that filtering by this method provides a reliable result that can serve as a reference for comparison with the proposed new filtering method.

The theoretical discussion presented in this article was verified with two practical examples. The results obtained from computation by the M_split method with appropriate terrain models encourage more detailed theoretical and empirical tests of this method for the filtering and segmentation of ALS data-sets.     

Automatic Surface Reconstruction from Point Sets in Space

In this paper an algorithm is proposed that takes as input a generic set of unorganized points, sampled on a real object, and returns a closed interpolating surface. Specifically, this method generates a closed 2-manifold surface made of triangular faces, without limitations on the shape or genus of the original solid. The reconstruction method is based on generation of the Delaunay tetrahedralization of the point set, followed by a sculpturing process constrained to particular criteria. The main applications of this tool are in medical analysis and in reverse engineering areas. It is possible, for example, to reconstruct anatomical parts starting from surveys based on TACs or magnetic resonance.     

Surface Reconstruction based on Lower Dimensional Localized Delaunay Triangulation

We present a fast, memory efficient algorithm that generates a manifold triangular mesh S passing through a set of unorganized points P R ³. Nothing is assumed about the geometry, topology or presence of boundaries in the data set except that P is sampled from a real manifold surface. The speed of our algorithm is derived from a projection-based approach we use to determine the incident faces on a point. We define our sampling criteria to sample the surface and guarantee a topologically correct mesh after surface reconstruction for such a sampled surface. We also present a new algorithm to find the normal at a vertex, when the surface is sampled according our given criteria. We also present results of our surface reconstruction using our algorithm on unorganized point clouds of various models.     

Multi-scale Feature Extraction on Point-Sampled Surfaces

We present a new technique for extracting line‐type features on point‐sampled geometry. Given an unstructuredpoint cloud as input, our method first applies principal component analysis on local neighborhoods toclassify points according to the likelihood that they belong to a feature. Using hysteresis thresholding, we thencompute a minimum spanning graph as an initial approximation of the feature lines. To smooth out the featureswhile maintaining a close connection to the underlying surface, we use an adaptation of active contour models.Central to our method is a multi‐scale classification operator that allows feature analysis at multiplescales, using the size of the local neighborhoods as a discrete scale parameter. This significantly improves thereliability of the detection phase and makes our method more robust in the presence of noise. To illustrate theusefulness of our method, we have implemented a non‐photorealistic point renderer to visualize point‐sampledsurfaces as line drawings of their extracted feature curves.     

3D medial axis point approximation using nearest neighbors and the normal field

We present a novel method to approximate medial axis points given a set of points sampled from a surface and the normal vectors to the surface at those points. For each sample point, we find its maximal tangent ball containing no other sample points, by iteratively reducing its radius using nearest neighbor queries. We prove that the center of the ball constructed by our algorithm converges to a true medial axis point as the sampling density increases to infinity. We also propose a simple heuristic to handle noisy samples. By simple extensions, our method is applied to medial axis point simplification, local feature size estimation and feature-sensitive point decimation. Our algorithm is simple, easy to implement, and suitable for parallel computation using GPU because the iteration process for each sample point runs independently. Experimental results show that our method is efficient both in time and in space.