基于移动激光扫描数据的建筑物精细化建模研究

随着"数字城市"的发展,各行各业对空间数据需求不断增高,三维建模是"数字城市"的重要组成部分,传统的建筑物三维建模方法耗时耗力、精度低、采用接触式测量,这种采集方法和数据处理方式已不能满足数字化的需要。移动激光扫描技术的出现,改变了传统的的数据采集方式,为空间三维信息的获取提供了全新的技术手段。通过移动激光扫描技术获取的空间点云数据,可以建立结构错综和不规则场景的建筑物模型。鉴于传统方法的不足和移动激光扫描技术的特点,提出移动扫描技术三维建模的方法,以云南师范大学商学院的激光扫描为例,首先通过将获取的点云数据的配准、拼接、降噪、POS解算、IE解算和点云数据融合,然后进行精细三维建模。     

基于区域结构特征的城区LiDAR数据快速分类

机载激光雷达能够及时准确地获取大量具有精确三维位置信息的三维点云数据,在数字城市、森林防火、智能交通等领域有着广泛的应用。城市中心区域的三维点云数据往往会因为高大树木或植被的遮挡,造成建筑物等人造目标识别特别困难。本文通过直接的二次多项式拟合方式提取高大树木等植被与建筑物目标典型局部区域的区域信息,构建区域目标敏感的结构特征,进而,通过模糊逻辑即可完成三维点云数据的建筑物目标敏感的分类任务。实验结果表明,该方法能够快速有效地实现较大尺度范围内LiDAR点云数据的分类,具有较好的应用前景和推广能力。     

激光点云提取建筑物平面目标算法研究

从激光点云中提取建筑物信息是当前遥感数据处理的热点与难点,而构成建筑物的平面以及轮廓线的提取是LIDAR数据处理和建筑物三维建模的关键技术。本文通过分析激光点云数据中建筑物的特征,综合点云滤波、KD树索引、三维Hough变换以及Gauss球法向量统计算法的各自优点,提出了一套建筑物平面及轮廓的自动提取算法,并通过实验验证了该算法的有效性。     

激光三维扫描图像处理改进算法

三维激光扫描技术以获取被测物体三维轮廓数据为目的,提取激光照射到被测物体形成的激光条纹的三维信息,从而快速获取被测物体表面的三维坐标数据.在图像处理中,经过去噪处理、图像灰度二值化、激光条纹细化等一系列改进的算法,较好地实现了激光条纹图像信息的提取,从而获得了较为理想的效果图.     

激光点云解算的软硬件协同设计与实现

机载激光雷达在测绘、勘探等领域有广泛的应用,其数据处理联合激光雷达测距数据和姿态位置信息,解算获得扫描目标的三维坐标并形成三维点云图。为了满足机载激光雷达点云解算的实时性要求,采用基于软硬件协同的设计方法,设计、实现了激光点云解算的SoC。通过使用基于AXI-4的DMA高速传输方式,运用流水线优化和存储优化方法,实现了高性能的硬件加速器。实验结果表明,提出的激光点云解算的SoC能够满足机载平台的实时性处理要求。     

基于QT的机载三维激光扫描仪软件系统设计

随着测绘技术的发展,利用机载三维激光扫描仪获取点云数据,从点云数据中提取模型信息成为现代测绘技术的一种发展趋势. 点云数据处理的相关应用也越来越多,点云数据的处理软件参差不齐,需求和功能也各不相同. 针对上述问题,在基于QT编程平台下,利用PCL开发机载三维激光扫描仪点云数据处理软件,集中处理点云数据. 该系统由点云数据读写、三维显示、点云滤波、网格重构、点拾取、点云分割及NARF关键点提取等功能模块组成. 各模块采用面向对象的思想设计,功能易于扩展,以完成进一步的测绘工作. 实践表明,该系统具有良好的工程实用价值,可以快速完成机载三维激光扫描仪的数据处理工作.     

从机载激光扫描数据中交互式提取人字形房屋模型的方法研究

快速有效地从机载激光扫描(airborne lidar)点云数据中提取房屋模型是机载激光扫描系统应用研究的一项重要课题。鉴于交互式半自动方法是从点云数据中提取简单规则房屋模型信息的一种可行的方法,为此采用3维空间中改进的Hough变换以及聚类分析,提出了一种从点云数据中交互式提取人字形房屋模型的方法。该方法分为3个步骤:第1步是用户确定房屋区域,并分割出候选的屋顶点集;第2步是对候选屋顶点集采用3维空间中改进的Hough变换,然后对Hough变换后所获得的参数集进行聚类分析,以此获得屋顶所在平面的参数表达;第3步是构造完整的房屋模型。通过屋顶平面相交得到屋脊线,通过点的范围分析确定屋顶的边缘,最后添加竖直的墙面构造完整的房屋模型。经采用Optech公司提供的数据进行实验初步证实,该方法是可行的,且整个提取过程只需要很少的用户交互,因此适合于大规模处理机载激光扫描数据。     

基于多尺度的城区机载点云滤波方法研究

针对城市地区机载激光扫描数据（ALS）中提取数字地面模型这一问题,提出了一种基于多尺度的由粗到细的滤波算法。通过计算每个点与其周围一定邻域内激光点的几何特征值关系,将点云粗分类为平面点、边缘点和离散点;对平面点进行区域跟踪,利用强度方差将平面点分类为地面点、建筑物点以及未确定类别点;对地面点构建Delaunay三角网,反向分析未确定点以及边缘点来加密地面点集。通过实验验证了该算法对城市地区滤波的有效性。     

基于点云数据的复杂建筑体真三维建模与应用

传统建筑物三维建模方法费时费力、精度低、采用接触式测量,且仅能获得建筑物少量特征点及线状数据。与传统测量方法相比,地面三维激光扫描技术(Terrestrial Laser Scanner,TLS)方法可快速、高效、非接触式地获取建筑物表面高精度三维信息,因此其较传统建筑物三维建模方法优势显著。以古田会议旧址为研究对象,首先介绍了研究目标的主要特征以及点云数据采集方案;其次以高复杂度建筑物建模为需求出发,详细阐述了点云数据预处理及建筑物三维模型重建相关核心技术及方法;并重点讨论了与其相关的点云数据配准拼接、去噪简化、二维轮廓线提取、三维实体重建;最后实现古田会议旧址高复杂度三维几何模型重现,并采用先进的3D打印技术按1∶40比例尺制作3D打印点云数据的微缩模型。通过与实地测量数据对比分析得知,采用地面三维激光测量方法采集点云数据的建筑物建模精度优于传统测量方法。此研究成果可应用于古田会议旧址等文化遗产的文物修复、变形监测、虚拟重现等方面。     

激光扫描历史建筑精细化重建与部件化管理

针对历史建筑三维重建的精度要求高、产生部件信息数量多的问题,提出地面激光扫描的高精度精细化模型重建的方法以及对模型进行部件化管理的策略。基于上海外滩历史建筑群,在三维激光点云数据所获取的高精度建筑物空间信息的基础上实施精细化模型重建,细化建筑物的空间信息;针对历史建筑的特点,对建筑物模型信息和对应的属性信息进行多层次部件化管理。最后以具有典型外滩历史建筑特征的亚细亚大楼为实例,基于以上方法构建历史建筑数字档案。成果表明,重建模型达到建筑保护的精度需求,以部件为基本单位的方式可以有效地组织和管理海量模型信息和属性信息,并且信息的统计、归纳、调用也更为便捷。     

Automatic detection of building roofs from point clouds produced by the dense image matching technique

Three-dimensional (3D) spatial information of object points is a vital requirement for many disciplines. Laser scanning technology and techniques based on image matching have been used extensively to produce 3D dense point clouds. These data are used frequently in various applications, such as the generation of digital surface model (DSM)/digital terrain model (DTM), extracting objects (e.g., buildings, trees, and roads), 3D modelling, and detecting changes. The aim of this study was to extract the building roof points automatically from the 3D point cloud data created via the image matching techniques with optical aerial images (with red, green, and blue band (RGB) and infrared (IR)). In the first stage of the study, as an alternative to laser scanning technology, which is more expensive than optical imaging systems, the 3D point clouds were produced by matching high-resolution images using a Semi Global Matching algorithm. The normalized difference vegetation index (NDVI) values for each point were calculated using the spectral information (RGB + IR) in the 3D point cloud data, and the points that represented the vegetation cover were determined using these values. In the second stage, existing ground and non-ground points that were free of vegetation cover were determined within the point cloud. Subsequently, only the points on the roof of the building were detected automatically using the proposed algorithm. Thus, points of the roofs of buildings located in areas with different topographic characteristics were detected automatically detected using only images. It was determined that the average values of correctness (Corr), completeness (Comp), and quality (Q) of the pixel-based accuracy analysis metrics were 95%, 98%, and 93%, respectively, in the selected test areas. According to the results of the accuracy analysis, it is clear that the proposed algorithm is very successful in automatic extraction of building roof points.     

基于高空间分辨率与立体像对遥感数据的建筑物三维信息提取

准确获取建筑的三维分布信息对于城市规划与管理、灾害风险评估与防范以及灾后救助等都具有非常重要的意义。针对目前建筑物信息提取研究集中于二维平面信息提取,三维信息提取研究较少,且方法自动化程度较低,实用性和和推广性不足,提出了综合立体像对和高空间分辨率两种遥感数据进行建筑物三维信息提取的方法。首先,基于小波变换融合方法对GeoEye\|1高空间分辨率全色和多光谱影像进行融合,然后运用面向对象方法对融合后的高空间分辨率遥感影像进行建筑物基底轮廓提取,再利用IRS\|P5立体像对反演地物高度,最后通过数据整合获得研究区建筑物的三维空间分布。研究结果表明:该方法可以充分利用不同遥感数据的优势,获得较高的提取精度;研究所需数据容易获取,方法具有较好的可操作性和推广性。     

Complete residential urban area reconstruction from dense aerial LiDAR point clouds

We present an automatic system to reconstruct 3D urban models for residential areas from aerial LiDAR scans. The key difference between downtown area modeling and residential area modeling is that the latter usually contains rich vegetation. Thus, we propose a robust classification algorithm that effectively classifies LiDAR points into trees, buildings, and ground. The classification algorithm adopts an energy minimization scheme based on the 2.5D characteristic of building structures: buildings are composed of opaque skyward roof surfaces and vertical walls, making the interior of building structures invisible to laser scans; in contrast, trees do not possess such characteristic and thus point samples can exist underneath tree crowns. Once the point cloud is successfully classified, our system reconstructs buildings and trees respectively, resulting in a hybrid model representing the 3D urban reality of residential areas.     

A new hierarchical moving curve-fitting algorithm for filtering lidar data for automatic DTM generation

Recent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection and ranging (lidar) points in areas covered by densely packed buildings or dense vegetation is difficult. In this paper, we introduce a new hierarchical moving curve-fitting filter algorithm that is designed to automatically and rapidly filter lidar data to permit automatic DTM generation. This algorithm is based on fitting a second-degree polynomial surface using flexible tiles of moving blocks and an adaptive threshold. The initial tile size is determined by the size of the largest building in the study area. Based on an adaptive threshold, non-ground points and ground points are classified and labelled step by step. In addition, we used a multi-scale weighted interpolation method to estimate the bare-earth elevation for non-ground points and obtain a recovered terrain model. Our experiments in four study areas showed that the new filtering method can separate ground and non-ground points in both urban areas and those covered by dense vegetation. The filter error ranged from 4.08% to 9.40% for Type I errors, from 2.48% to 7.63% for Type II errors, and from 5.01% to 7.40% for total errors. These errors are lower than those of triangulated irregular network filter algorithms.     

Complete classification of raw LIDAR data and 3D reconstruction of buildings

LIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents a three-stage framework for a robust automatic classification of raw LIDAR data as buildings, ground and vegetation, followed by a reconstruction of 3D models of the buildings. In the first stage the raw data are filtered and interpolated over a grid. In the second stage, first a double raw data segmentation is performed and then geometric and topological relationships among regions resulting from segmentation are computed and stored in a knowledge base. In the third stage, a rule-based scheme is applied for the classification of the regions. Finally, polyhedral building models are reconstructed by analysing the topology of building outlines, building roof slopes and eaves lines. Results obtained on data sets with different ground point density, gathered over the town of Pavia (Italy) with Toposys and Optech airborne laser scanning systems, are shown to illustrate the effectiveness of the proposed approach.     

Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection

A method for the detection of buildings in densely built-up urban areas by the fusion of first and last pulse laser scanner data and multi-spectral images is presented. The method attempts to achieve a classification of land cover into the classes “building”, “tree”, “grassland”, and “bare soil”, the latter three being considered relevant for the subsequent generation of a high-quality digital terrain model (DTM). Building detection is accomplished by first applying a hierarchical rule-based technique for coarse DTM generation based on morphological filtering. After that, data fusion based on the theory of Dempster–Shafer is used at two different stages of the classification process. We describe the algorithms involved, giving examples for a test site in Fairfield (New South Wales).     

一种城市三维建模的集成处理方法

在对AutoCAD和MapInfo的数据结构进行分析比较的基础上，首先提出了从CAD图形符合及注记中，提高取程点、高程值以及相关属性信息的面积匹配与类型转换的方法；然后创建了基于CAD线划建筑物的重现点-线拓扑关系生成多边形的方法；最后实现了地形高程与建筑物空间及其属性特征值的自动获取，在此基础上进一步构建了城市DEM模型和实现了三维影象显示，形成了由CAD数据→GIS矢量数据→GIS栅格数据→三维影像的一种城市三维建模的集成处理方法，该方法较之工程化的航空摄影测量方法简单、快速、具有较大的实有价值，另外，还以苏州市32 号街坊的地形数据为例进行了实验，获得了较好的实验结果。     

Bare-earth extraction and DTM generation from photogrammetric point clouds including the use of an existing lower-resolution DTM

A method of extracting bare-earth points from photogrammetric point clouds by partially using an existing lower resolution digital terrain model (DTM) is presented. The bare-earth points are extracted based on a threshold defined by local slope. The local slope is estimated from the lower resolution DTM. A gridded DTM is then interpolated from the extracted bare-earth points. Five different interpolation algorithms are implemented and evaluated to identify the most suitable interpolation method for such non-uniformly scattered data. The algorithm is tested on four test sites with varying topographic and ground cover characteristics. The results are evaluated against a reference DTM created using aerial laser scanning. The deviations of the extracted bare-earth points, and the interpolated DTM, from the reference DTM increases with increasing forest canopy density and terrain roughness. The DTM created by the method is significantly closer to the reference DTM than the lower resolution national DTM. The ANUDEM (Australian National University Digital Elevation Modelling) interpolation method is found to be the best performing interpolation method in terms of reducing the deviations and in terms of modelling the terrain realistically with minimum artefacts, although the differences among the interpolation methods are not considerably large.     

空地融合的城市道路基础设施数字化技术研究

城市道路基础设施三维模型的重构,在城市道路BIM应用与数字化领域具有重大意义;针对城市交通基础设施数字化重构的需求,对车载激光扫描与无人机倾斜摄影采集技术进行综合运用,提出一套从信息采集、空地点云配准、点云分割到三维重构的完整技术方法;首先使用车载激光扫描技术和无人机倾斜摄影技术对交通基础设施信息进行采集,并使用运动恢复结构算法(SfM,structure from motion)生成基础设施空地点云;其次使用迭代最近点法(ICP,iterative closest point)对空地点云进行精配准,然后利用基于PointNet网络的方法对融合点云进行语义分割;最后对分割出的交通基础设施对象进行三维重构;提出的空地融合的城市交通基础设施数字化技术能够高效地实现交通基础设施重构,为城市交通基础设施数字化提供基础、为后续交通专业领域的应用研究提供便利.     

Urban building reconstruction from raw LiDAR point data

We present a method for automatic reconstruction of the volumetric structures of urban buildings, directly from raw LiDAR point clouds. Given the large-scale LiDAR data from a group of urban buildings, we take advantage of the “divide-and-conquer” strategy to decompose the entire point clouds into a number of subsets, each of which corresponds to an individual building. For each urban building, we determine its upward direction and partition the corresponding point data into a series of consecutive blocks, achieved by investigating the distributions of feature points of the building along the upward direction. Next, we propose a novel algorithm, Spectral Residual Clustering (SRC), to extract the primitive elements within the contours of blocks from the sectional point set, which is formed by registering the series of consecutive slicing points. Subsequently, we detect the geometric constraints among primitive elements through individual fitting, and perform constrained fitting over all primitive elements to obtain the accurate contour. On this basis, we execute 3D modeling operations, like extrusion, lofting or sweeping, to generate the 3D models of blocks. The final accurate 3D models are generated by applying the union Boolean operations over the block models. We evaluate our reconstruction method on a variety of raw LiDAR scans to verify its robustness and effectiveness.