Knowledge based reconstruction of building models from terrestrial laser scanning data

This paper presents an automatic method for reconstruction of building façade models from terrestrial laser scanning data. Important façade elements such as walls and roofs are distinguished as features. Knowledge about the features’ sizes, positions, orientations, and topology is then introduced to recognize these features in a segmented laser point cloud. An outline polygon of each feature is generated by least squares fitting, convex hull fitting or concave polygon fitting, according to the size of the feature. Knowledge is used again to hypothesise the occluded parts from the directly extracted feature polygons. Finally, a polyhedron building model is combined from extracted feature polygons and hypothesised parts. The reconstruction method is tested with two data sets containing various building shapes.     

Building roof modeling from airborne laser scanning data based on level set approach

This paper presents a novel approach to building roof modeling, including roof plane segmentation and roof model reconstruction, from airborne laser scanning data. Segmentation is performed by minimizing an energy function formulated as multiphase level set. The energy function is minimized when each segment corresponds to one or several roof plans of the same normal vector. With this formulation, maximum n regions are segmented at a time by applying log₂n level set functions. The roof ridges or step edges are then delineated by the union of the zero level contours of the level set functions. In the final step of segmentation, coplanar and parallel roof segments are separated into individual roof segments based on their connectivity and homogeneity. To reconstruct a 3D roof model, roof structure points are determined by intersecting adjacent roof segments or line segments of building boundary and then connected based on their topological relations inferred from the segmentation result. As a global solution to the segmentation problem, the proposed approach determines multiple roof segments at the same time, which leads to topological consistency among the segment boundaries. The paper describes the principle and solution of the multiphase level set approach and demonstrates its performance and properties with two airborne laser scanning data sets.     

机载激光雷达数据的建筑物三维模型重建

机载激光雷达技术可以快速获取建筑物高精度高密度的三维空间信息.文章提出一种建筑物三维模型重建方法,该方法利用栅格数据使用RANSAC算法提取建筑边界上的长线段,采用规则化策略获取建筑轮廓;在模型生成阶段,基于原始点云和屋顶拓扑关系约束,获取屋顶间的交线和交点,并结合已得到的建筑边界、屋顶间交线和交点获取三维建筑模型.实验结果表明,该方法思路清晰、科学可行且适用性强,提取的建筑边界和模型垂直精度高.     

An update on automatic 3D building reconstruction

The development of tools for the generation of 3D city models started almost two decades ago. From the beginning, fully automatic reconstruction systems were envisioned to fulfil the need for efficient data collection. However, research on automatic city modelling is still a very active area. The paper will review a number of current approaches in order to comprehensively elaborate the state of the art of reconstruction methods and their respective principles. Originally, automatic city modelling only aimed at polyhedral building objects, which mainly reflects the respective roof shapes and building footprints. For this purpose, airborne images or laser scans are used. In addition to these developments, the paper will also review current approaches for the generation of more detailed facade geometries from terrestrial data collection.     

基于三维激光扫描点云数据的古建筑建模

针对传统古建筑数字化技术的不足和接触测量对建筑物造成"二次伤害",以西北民族大学蝴蝶厅异地重建项目为例,提出了一种结合三维激光扫描技术和现代测量技术以及本体思维进行室内外一体化三维建模的研究思路。首先给出了基于三维激光扫描技术的建模流程;其次,对点云数据处理和三维实体重建两个核心步骤,重点对点云数据的配准拼接、去噪简化和提取轮廓线、三维几何建模和纹理贴图等步骤进行详细论述。本体思维用于基于三维激光扫描的古建筑三维建模,可以有效指导古建筑构件分类建模,提高建模效率。结果表明:三维激光扫描技术适用于具有高复杂度几何特征的蝴蝶厅精细化、真实感建模,为西北民族大学蝴蝶厅古建筑文化遗产未来的开发、保护、维修、恢复重建与虚拟地理环境等提供高精度的数据基准。     

Robust statistical approaches for local planar surface fitting in 3D laser scanning data

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods.     

基于三维激光扫描仪的校园建筑物建模研究

三维激光扫描仪可以连续、自动、快速获取目标物表面的采样点数据。论述三维激光扫描仪工作原理、数据处理流程。以校园建筑物为例,给出三维数据获取、数据处理、模型建立的基本方法和结果。探讨采用点云数据进行数字校园的方法。     

基于三维激光扫描数据的三维实景构建

利用三维激光扫描仪获取的空间数据,进行三维实景构建研究.首先对扫描仪获取的数据按照基于格网的方法进行压缩,然后对压缩后的数据建立三角网数字地面模型,最后将原始空间数据按照一定的拓扑结构映射到二维球面(α,θ面)并在α,θ面等角划分形成二维矩阵,以落在矩阵各栅格内的点云强度均值作为该处像素值以图像形式显示,再同实际影像比较,选出同名点,根据摄影测量后方交会原理计算得到影像的内外方位元素,由得到的内外方位元素计算出相应的纹理坐标,完成纹理映射过程.文中以Trimble地面三维激光扫描仪为例以地物作为采集对象,用Visual Studi0 6.0作为开发平台进行三维实景构建.     

三维激光扫描点云数据压缩方法

三维激光扫描获得的点云数据,其数据量比较大,通过分析三维激光扫描数据的特点,本文提出了两个数据压缩方法,分别为区域重心数据压缩法和基于三维TIN数据的共顶点数据压缩法,介绍了两种数据压缩方法的原理和实现过程,并对区域重心数据压缩法进行实例分析,得出了压缩结果。     

利用三维激光扫描数据进行建筑物立面点云分割算法分析

三维激光扫描技术在当今社会的应用越来越广泛,但由于点云数据量大,处理效率低下,如何快速高效地将大量点云数据进行重建与识别成为解决问题的关键。点云分割技术能够将立面点云中的特征信息与背景点云分离开来,为地物特征信息的提取和识别工作提供了重要的技术支持。本文通过编程实现了多种点云分割算法,对建筑物立面进行分割处理,详细分析了不同算法的分割精度及适用范围。     

三维激光扫描技术在建筑立面照明设计中的应用

建筑立面图是立面照明设计的基础数据,直接影响着设计精度和效果。本文针对传统立面测量效率低、还原度低等问题,提出了基于三维激光扫描技术的建筑立面测量方案;并结合实际案例,探讨了导线式三维激光扫描和点云图形绘制。应用案例表明,三维扫描技术更便捷、高效地为立面照明设计提供了精确详细的建筑立面图。     

地面三维激光扫描数据拼接方法评估

点云数据拼接方法直接关系到地面三维激光扫描仪的测量精度和效率,通过实验对5种拼接方法的精度进行评估.结果表明,50 m测量距离内,5种点云数据拼接方法的精度分别为1.33 mm、8.88 mm、2.71 mm、4.29 mm和6.07 m m,其中标靶拼接的精度最高.综合评估拼接方法的精度、效率,为实际工程提供参考方案.     

三维激光扫描系统点云数据向AutoCAD数据格式的转换

Cyra三维激光扫描系统是一种先进的测绘仪器,但由于它所采集的数据量过于巨大,当需要将它采集的点云数据在AutoCAD平台进行处理时,普通的个人电脑还无法运行如此海量的数据,以在辽宁省阜新市热电厂采集的管线数据为例,采用数据分割、稀化等手段,解决点云数据在AutoCAD平台上无法正常应用的问题,总结出一种切实可行的技术方法。     

三维激光扫描点云混合像素的自动识别方法

混合像素的存在会给点云后续处理与应用带来干扰,针对如何在原始扫描点云中自动识别混合像素的问题,从扫描仪的工作原理着手分析了混合像素的形成条件,结合扫描仪的内部光电转换机制与真实扫描数据论证混合像素的测距误差分布规律,据此设计混合像素的自动探测方法。利用真实原始扫描数据进行实验,结果证明,文中方法能够准确地识别出点云中的混合像素。     

三维激光扫描技术在历史建筑立面测绘中的应用北大核心CSCD

随着现代化科技手段的进步与更新,利用三维激光扫描实景复制新型技术原理替代传统式单点立面测绘的技术手段,对历史建筑进行保护,能最大化节省人工成本,提高工作效率。能够将细节、精度、效率统一达到产能最大化,也能提供更高级且全面的原始数据存档及成果交付方式,不断更新历史建筑数字化的技术标准。     

三维激光扫描技术在建筑立面测绘中的应用

针对传统建筑立面测量方法作业效率低下问题,笔者探索出基于三维激光扫描技术快速实现建筑立面测绘的应用方案。本文结合实际场景,通过天宝TX8激光扫描仪获取建筑点云数据,利用Trimble RealWorks软件进行点云绘图。结果表明,地面激光扫描技术可以高效地实现建筑立面测绘,具有一定的推广意义。     

三维激光点云和无人机倾斜模型的融合应用

在对建筑物进行三维实景数据采集时,地面三维激光扫描和无人机倾斜摄影测量是常用的技术手段,通过两种手段获取的数据类型不同,对空间的展示效果及精度也存在差异,特别是在获取建筑物顶部和近地部分的数据各有优劣势,将无人机获取的建筑物顶部数据和地面三维激光扫描获取的近地部分数据进行处理,通过空间多个同名公共点进行转换、匹配,将点...     

基于三维激光扫描技术的人防工事模型重构与应用分析

人防工事狭长且内部环境复杂,对其保护利用需要提供丰富多样的基础资料和成果。三维激光扫描技术突破了传统单点精确测量的局限,可以大面积地获取被测对象表面的三维坐标数据,具有连续、快速、非接触、自动、全天候工作等优势。本文以某段人防工事为例,研究了三维激光扫描的工作流程,并以点云数据为基础进行三维模型的重构和测绘图的制作,完成对点云质量的分析评价。     

快速处理大数据量三维激光扫描数据的技术研究

为解决由于利用三维激光扫描仪进行模型重建过程中数据量大而造成的在数据读取、数据处理及三维显示等方面速度低下、操作不流畅等问题,结合当前计算机软、硬件技术的现状,分析激光扫描数据本身的特点,提出基于内存映射文件技术、虚拟内存技术、多线程技术、数据库技术及用OpenGL快速显示三维模型的技术等解决手段,同时在WindowsXP操作系统上,利用Vc++6.0给予了具体的程序实现,以古建筑室内激光扫描数据作为试验数据,实践表明了文章提出的技术手段在现有的计算机条件下,较好地解决数据量大、数据处理和显示速度慢的问题。