基于扫描点云数据的零部件边缘轮廓提取技术

针对目前大型零部件边缘轮廓线提取效率低的问题,提出一种基于扫描点云数据的零部件边缘轮廓提取技术.该技术从边缘轮廓线的形成方式出发,通过分析边缘轮廓线垂直截面线的分布特征,定义了模式向量表征屋脊型和折线直线-直线相交型截面线.提取过程主要包含截面线数据的获取、截面线类型的识别和边缘轮廓线数据的提取这3个步骤.为获取边缘轮廓线的截面线数据,构造了初始边缘轮廓线,并沿初始轮廓线构造垂直万向切片.万向切片与网格化点云数据的交点即为截面线数据.定义了基于相邻点法向夹角的模式向量,用以识别截面线的类型.针对折线型和屋脊型截面线数据提出拟合求交法和重心计算法提取其上的边缘轮廓线数据点.开展试验验证提取技术的有效性和精度,结果表明所述方法的边缘轮廓线提取准确度可达到±0.2 mm.     

基于点云切片的边界提取

针对点云切片的截面数据可能存在不连续的特征曲线对拟合造成误差的情况,提出对每个截面数据将不连续的特征曲线点集分割成连续的特征曲线点集的方法。鉴于不连续特征曲线点集之间、点与点之间的欧式距离远大于点云密度,而同一个连续特征曲线点集中点与点的欧式距离趋近于点云密度,所以可以利用点云密度作为阈值,将不同的连续特征曲线点集分割开。对每个连续的特征曲线点集进行曲线拟合,得到点云切片的边界。分析结果表明,该分割算法能有效地分割出点云切片中不连续的特征曲线点集,减小曲线拟合时的误差,提高了点云切片边界提取的精度。     

反求工程中的混合切片技术

提出一种基于平面与“点云”、平面与NURBS曲面求交计算的混合切片方法．该方法可以保证切片曲线在点云和曲面的连接处达到G^1连续，在此基础上的重构曲面既能保证与相邻曲面的连续性要求，又能满足对点云的逼近精度要求，对反求建模尤其是过渡特征的重建有着重要意义．文中详细探讨了平面与曲面求交和点云切片两个核心算法，并对基于模型特征的混合切片方案的选择原则以及不同方法进行了论述和比较．最后用实例证明该方法在反求建模中是切实可行的．     

股骨解剖结构的B样条曲面重构

按照曲线复杂度定义，首先对复杂度最高的曲线进行最精简逼近，然后在一定控制精度下对轮廓线依据曲率特征进行重采样；对所有曲线进行基于曲率特征的重采样，并对曲线采用插值方法统一轮廓点数量；采用平均化的方法获得U，V方向的公共节点矢量，并通过插值的方法得到体积较小的曲面造型．避开了生成相容性轮廓线的过程，提高了曲面重构的效率．     

基于聚类平面特征的三维点云数据精简算法

根据建筑物在高度方向截面上的点云数据必定位于其轮廓线的原理,提出基于聚类平面特征的点云数据精简算法。该算法无需对扫描对象进行表面重构,而是在保持建筑物高度方向数据精度的前提下,对点云数据分层聚类简化,保留满足条件的特征点,删除其余的点。通过实例证明该算法可以在保持建筑物外形特征的同时,达到较高的精简比率。     

逆向工程中平面轮廓线数据的B样条曲面拟合

利用统计学的知识，对空间数据点参数值分布情况进行了分析，给出了曲面拟合过程中节点矢量的确定方法。在此基础上给出了曲面轮廓线数据的B样条曲面拟合算法以及应用该方法对平面轮廓线扫描数据进行B样条曲面拟合的算例。     

基于切片方法截面数据处理

为了提高后续截面轮廓重建的精度,提出了基于截面切片后数据处理的系列算法.首先用点云束细化算法对切片数据进行细化处理,采用类似于移动最小二乘法的跟踪方法,整个过程不对测量数据进行局部坐标变换,迭代步长由点云密度控制;将截面切片数据细化后,用双链表排序算法对细化后的数据进行排序处理;对截面测量数据的特征点提取,结合"角偏差法"和"弦高差法"的优点,研究了对提取特征点结果影响的几个主要因子,提出一种对冗余数据处理及特征点提取的方法,得到的点云数据可以进行很好的分组处理,并拟合成合适的轮廓特征单元.     

带自由曲面的三视图重建算法研究

针对蕴含自由曲面的三视图,提出了通过恢复边界线和轮廓线进而根据这些恢复 的空间曲线重建自由曲面的算法。首先分析并证明了自由曲面在三视图中的投影性质,从而提 出边界投影和轮廓投影的匹配算法。针对视图中存在的被打断的样条曲线,提出了分段样条曲 线的爬坡算法来解决此类曲线不能匹配的问题。然后基于投影匹配序列重建出自由曲面的边界 线,再由边界投影上的点和轮廓投影端点的对应关系重建出空间轮廓线。由轮廓线等参采样构 造截面线并和边界线一起蒙皮生成最终自由曲面。本文提出的算法扩展了工程图的重建域。     

一种RE／RP集成的工艺产品原型快速开发方法

针对工艺产品表面特征复杂的特点，本文提出了一种RE／RP集成的快速原型开发方法：对扫描测量得到的表面点云数据直接进行切层处理，根据截面切点数据进行轮廓的识别与提取，获得产品的截面轮廓信息，输出SLC格式切片文件给快速成型系统制造产品的物理原型件。     

一种RE/RP集成的工艺产品原型快速开发方法

针对工艺产品表面特征复杂的特点，本文提出了一种ＲＥ／ＲＰ集成的快速原型开发方法：对扫描测量得到的表面点云数据直接进行切层处理，根据截面切点数据进行轮廓的识别与提取，获得产品的截面轮廓信息，输出ＳＬＣ格式切片文件给快速成型系统制造产品的物理原型件。     

杂乱点云的快速曲线拟合算法研究

针对杂乱点云的曲线拟合问题，根据Bezier曲线原理，给出了一种改进的曲线拟合算法。算法在保持曲面特征基本信息的前提下，先对测量到的杂乱点云进行平行切割，再对切割后的截线云进行插入排序，然后从中选点用Bezier曲线来拟合、逼近截线云。实践证明，该算法高效、稳定，可以快速地重构出三维形体的特征曲线。     

Feature Preserving Mesh Generation from 3D Point Clouds

We address the problem of generating quality surface triangle meshes from 3D point clouds sampled on piecewise smooth surfaces. Using a feature detection process based on the covariance matrices of Voronoi cells, we first extract from the point cloud a set of sharp features. Our algorithm also runs on the input point cloud a reconstruction process, such as Poisson reconstruction, providing an implicit surface. A feature preserving variant of a Delaunay refinement process is then used to generate a mesh approximating the implicit surface and containing a faithful representation of the extracted sharp edges. Such a mesh provides an enhanced trade‐off between accuracy and mesh complexity. The whole process is robust to noise and made versatile through a small set of parameters which govern the mesh sizing, approximation error and shape of the elements. We demonstrate the effectiveness of our method on a variety of models including laser scanned datasets ranging from indoor to outdoor scenes.     

Outlier detection for scanned point clouds using majority voting

When scanning an object using a 3D laser scanner, the collected scanned point cloud is usually contaminated by numerous measurement outliers. These outliers can be sparse outliers, isolated or non-isolated outlier clusters. The non-isolated outlier clusters pose a great challenge to the development of an automatic outlier detection method since such outliers are attached to the scanned data points from the object surface and difficult to be distinguished from these valid surface measurement points. This paper presents an effective outlier detection method based on the principle of majority voting. The method is able to detect non-isolated outlier clusters as well as the other types of outliers in a scanned point cloud. The key component is a majority voting scheme that can cut the connection between non-isolated outlier clusters and the scanned surface so that non-isolated outliers become isolated. An expandable boundary criterion is also proposed to remove isolated outliers and preserve valid point clusters more reliably than a simple cluster size threshold. The effectiveness of the proposed method has been validated by comparing with several existing methods using a variety of scanned point clouds.     

古遗址多视点云的渐进式三维配准*

为实现古遗址场景多分辨率点云的精确配准,提出一种基于序列迭代的多视点云三维配准方法。利用反向投影确定两点集的重叠区,基于法向量计算定标球球心坐标,实现相邻点云的准确融合;利用基于马氏距离计算相邻点云的三维边缘点,通过在迭代过程中不断优化转换矩阵,逐步提高配准精度,消除部分重影现象。唐小雁塔和西汉古墓的工程实践表明,该方法能有效抑制序列配准的累积误差,具有有效性和鲁棒性,可应用于古遗址的数字化保护与虚拟展示领域。     

基于曲率法线流的树点云骨架提取方法

针对树木点云拓扑结构复杂、特征细节繁多等问题,提出一种基于点云收缩提取曲线骨架的算法。首先,为了在点云表面直接应用网格收缩算法,对点云进行局部主成分分析和Delaunay三角剖分;其次,针对树木点云拓扑结构复杂和末枝细节繁多等问题,用曲率法线流算子对点云进行收缩,针对树木枝条细长且弯曲幅度平缓等特点,利用改进后的QEM网格简化方法将三角网格折叠成一维曲线骨架;最后,将得到的曲线骨架进行连通和居中处理。该算法直接在点云上进行操作,不需要额外的信息和预处理操作,对噪声和残缺点云有良好的鲁棒性。实验证明,该算法提取的树木点云骨架充分表达了树木在自然环境下的生物性结构和特征,相对于rosa、L1-中轴等经典算法,在树木点云的骨架提取速度上提高3倍以上,枝条重建度提高25%。     

Skeleton-based intrinsic symmetry detection on point clouds

We present a skeleton-based algorithm for intrinsic symmetry detection on imperfect 3D point cloud data. The data imperfections such as noise and incompleteness make it difficult to reliably compute geodesic distances, which play essential roles in existing intrinsic symmetry detection algorithms. In this paper, we leverage recent advances in curve skeleton extraction from point clouds for symmetry detection. Our method exploits the properties of curve skeletons, such as homotopy to the input shape, approximate isometry-invariance, and skeleton-to-surface mapping, for the detection task. Starting from a curve skeleton extracted from an input point cloud, we first compute symmetry electors, each of which is composed of a set of skeleton node pairs pruned with a cascade of symmetry filters. The electors are used to vote for symmetric node pairs indicating the symmetry map on the skeleton. A symmetry correspondence matrix (SCM) is constructed for the input point cloud through transferring the symmetry map from skeleton to point cloud. The final symmetry regions on the point cloud are detected via spectral analysis over the SCM. Experiments on raw point clouds, captured by a 3D scanner or the Microsoft Kinect, demonstrate the robustness of our algorithm. We also apply our method to repair incomplete scans based on the detected intrinsic symmetries.     

Research on geometric features and point cloud properties for tree skeleton extraction

To solve skeleton extraction problems in the tree point cloud model, branch geometric features and local properties of point cloud are utilized to optimize tree skeleton extraction. First of all, according to the attribute information estimation and normal vector adjustment of point cloud neighbor domain, branch segmentation is made by estimated values and geometric features. Skeleton nodes are extracted in the branch subset in segmentations. Then, a graph is constructed based on skeleton node set and tree skeleton is reconstructed in this weighted directed graph. Finally, according to the tree growth characteristics, cubic Hermite curves are utilized to optimize the skeleton curve. This method is applied in the point cloud model of three-kind trees and it is compared with the skeleton extraction method based on voxel switch and point cloud contraction. The experiment results show that this method displays strong anti-interference and high-precision characteristics at branch bifurcation and crossed ending parts of fine tree branches. Thus, features of tree branches can be described more perfectly, obtaining the skeleton curve closer to the main axis.     

伪点云修正增强激光雷达数据

目的 激光雷达在自动驾驶中具有重要意义,但其价格昂贵,且产生的激光线束数量仍然较少,造成采集的点云密度较稀疏。为了更好地感知周围环境,本文提出一种激光雷达数据增强算法,由双目图像生成伪点云并对伪点云进行坐标修正,进而实现激光雷达点云的稠密化处理,提高3D目标检测精度。此算法不针对特定的3D目标检测网络结构,是一种通用的点云稠密化方法。方法 首先利用双目RGB图像生成深度图像,根据先验的相机参数和深度信息计算出每个像素点在雷达坐标系下的粗略3维坐标,即伪点云。为了更好地分割地面,本文提出了循环RANSAC （random sample consensus）算法,引入了一个分离平面型非地面点云的暂存器,改进复杂场景下的地面分割效果。然后将原始点云进行地面分割后插入KDTree （k-dimensional tree）,以伪点云中的每个点为中心在KDTree中搜索若干近邻点,基于这些近邻点进行曲面重建。根据曲面重建结果,设计一种计算几何方法导出伪点云修正后的精确坐标。最后,将修正后的伪点云与原始激光雷达点云融合得到稠密化点云。结果 实验结果表明,稠密化的点云在视觉上具有较好的质量,物体具有更加完整的形状和轮廓,并且在KITTI （Karlsruhe Institute of Technology and Toyota Technological Institute）数据集上提升了3D目标检测精度。在使用该数据增强方法后,KITTI数据集下AVOD （aggregate view object detection）检测方法的AP_3D-Easy （average precision of 3D object detection on easy setting）提升了8.25%,AVOD-FPN （aggregate view object detection with feature pyramid network）检测方法的AP_BEV-Hard （average precision of bird’s eye view on hard setting）提升了7.14%。结论 本文提出的激光雷达数据增强算法,实现了点云的稠密化处理,并使3D目标检测结果更加精确。