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1.
Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (map/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection in building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models (DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is then clustered with the Self-organizing Map (SOM), with “change”, “non-change” and “uncertain change” status labeled through a voting strategy. The “uncertain changes” are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are extracted combining the multispectral images and the DSM by morphological operators, and the new buildings are determined by excluding the verified unchanged buildings from the second step. Both the synthetic experiment with Worldview-2 stereo imagery and the real experiment with IKONOS stereo imagery are carried out to demonstrate the effectiveness of the proposed method. It is shown that the proposed method can be applied as an effective way to monitoring the building changes, as well as updating 3D models from one epoch to the other. 相似文献
2.
建筑物的三维建模是城市三维建模和可视化的重要组成部分。本文提出一种基于点云数据与遥感图像的建筑物三维模型快速建模方法。首先,运用改进的RANSAC法从点云数据中提取建筑立面,根据立面区分平顶建筑与人字形屋顶建筑;在此基础上,进一步对建筑物的高度进行提取;之后,利用区域增长法从遥感图像中提取建筑物屋顶轮廓,利用形态学方法对提取出的轮廓进行规则化处理,并基于Freeman链码提取轮廓角点,得到规整的轮廓;最后,根据提取出的建筑高度属性对屋顶轮廓拉伸并进行纹理映射,实现对建筑物的三维重建。通过实例证明,提出的方法能快速、高效地实现建筑物三维模型的重建。 相似文献
3.
现有密集匹配点云数据已实现了地表3维信息的精细化表达,然而由于误匹配,此类点云往往包含一定数量粗差点并影响后续应用的处理效果。针对此类数据中误匹配所产生粗差点的剔除问题,将变差甬数引入移动最小二乘(MLS)粗差剔除算法。变差函数对MLS拟合区域内点对间的相关性进行估算,以此为依据设置权值对最小二乘的结果进行优化;然后利用MLS局部、分区域地对点云进行曲面拟合;最终根据拟合结果剔除粗差。利用A3数字测图系统生成的城区、山区密集点云数据进行实验,并将处理结果、等权MLS处理结果与人工剔除结果进行对比。实验结果表明该算法可有效对点云中的粗差进行剔除,相较于等权MLS粗差剔除算法,陔算法在城区、山区的误判率分别降低了5.16%和1.31%。 相似文献
4.
S. Hinz M. Stephani L. Schiemann K. Zeller 《ISPRS Journal of Photogrammetry and Remote Sensing》2009,64(3):297-307
This article presents a modular photogrammetric recording and image analysis system for inspecting the material characteristics of transparent foils, in particular Ethylen–TetraFluorEthylen-Copolymer (ETFE) foils. The foils are put under increasing air pressure and are observed by a stereo camera system. Determining the time-variable 3D shape of transparent material imposes a number of challenges: especially the automatic point transfer between stereo images and, in temporal domain, from one image pair to the next. We developed an automatic approach that accommodates for these particular circumstances and allows reconstruction of the 3D shape for each epoch as well as determining 3D translation vectors between epochs by feature tracking. Examples including numerical results and accuracy measures prove the applicability of the system. 相似文献
5.
车载激光点云道路边界提取的Snake方法 总被引：2，自引：0，他引：2
针对车载激光点云中道路边界提取困难,自动化程度低的问题,提出一种基于离散点Snake的车载激光点云道路边界提取方法。不同于传统基于图像建立Snake,本文直接基于离散点建立Snake模型。先利用伪轨迹点数据,确定初始轮廓位置,参数化不同类型的道路边界初始轮廓;然后基于离散点构建适合多类型道路边界的Snake模型,定义模型内部、外部和约束能量,通过能量函数最小化推动轮廓曲线移动到显著道路边界特征点处,实现不同道路边界的精细提取。本文试验采用3份不同城市场景的车载激光点云数据验证本文方法的有效性,道路边界提取结果的准确率达到97.62%,召回率达到98.04%,F1-Measure值达到97.83%以上,且提取的道路边界结果与软件交互提取的结果有较好的吻合度。试验结果表明,本文方法能够修正噪声、断裂等数据质量对道路边界提取的影响,能够实现各类复杂城市环境中不同形状道路边界的提取,具有较强的稳健性和适用性。 相似文献
6.
A mobile laser scanning (MLS) system allows direct collection of accurate 3D point information in unprecedented detail at highway speeds and at less than traditional survey costs, which serves the fast growing demands of transportation-related road surveying including road surface geometry and road environment. As one type of road feature in traffic management systems, road markings on paved roadways have important functions in providing guidance and information to drivers and pedestrians. This paper presents a stepwise procedure to recognize road markings from MLS point clouds. To improve computational efficiency, we first propose a curb-based method for road surface extraction. This method first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation-difference thresholds. Next, points belonging to the extracted road surface are interpolated into a geo-referenced intensity image using an extended inverse-distance-weighted (IDW) approach. Finally, we dynamically segment the geo-referenced intensity image into road-marking candidates with multiple thresholds that correspond to different ranges determined by point-density appropriate normality. A morphological closing operation with a linear structuring element is finally used to refine the road-marking candidates by removing noise and improving completeness. This road-marking extraction algorithm is comprehensively discussed in the analysis of parameter sensitivity and overall performance. An experimental study performed on a set of road markings with ground-truth shows that the proposed algorithm provides a promising solution to the road-marking extraction from MLS data. 相似文献
7.
近年来,架站式三维激光扫描技术广泛应用于盾构隧道收敛监测,但频繁换站降低了外业测量的效率。同时,在激光点云拟合计算收敛点坐标时,收敛点位置会随着隧道变形而变化,因而,点云曲线拟合难以确保得到不同时间、同断面、同收敛点坐标,影响了收敛值的精度。自主研发了一款经济适用且高效可靠的移动激光扫描监测系统,解决了架站式三维激光扫描监测效率低下的问题。针对收敛值计算方法存在的问题,详细讲解了基于隧道平铺图的收敛值计算方法,具体过程如下:首先,经过点云标准圆投影并展开,再经过图像栅格化、内插及增强处理得到隧道平铺图;其次,通过图像二值化、连通域分析、自动识别及人工纠正环缝,准确划分隧道每环点云;接下来,绘制每环管片接缝在平铺图的位置,求出每环收敛定点方位;最后,通过分管片拟合,计算收敛定点坐标并推出收敛值。计算实例表明,移动激光扫描收敛值重复结果差值均在1.5 mm以内,由此可知,提出的计算方法可用于单圆盾构隧道进行高效收敛监测,且精度可靠。 相似文献
8.
Accurate 3D road information is important for applications such as road maintenance and virtual 3D modeling. Mobile laser scanning (MLS) is an efficient technique for capturing dense point clouds that can be used to construct detailed road models for large areas. This paper presents a method for extracting and delineating roads from large-scale MLS point clouds. The proposed method partitions MLS point clouds into a set of consecutive “scanning lines”, which each consists of a road cross section. A moving window operator is used to filter out non-ground points line by line, and curb points are detected based on curb patterns. The detected curb points are tracked and refined so that they are both globally consistent and locally similar. To evaluate the validity of the proposed method, experiments were conducted using two types of street-scene point clouds captured by Optech’s Lynx Mobile Mapper System. The completeness, correctness, and quality of the extracted roads are over 94.42%, 91.13%, and 91.3%, respectively, which proves the proposed method is a promising solution for extracting 3D roads from MLS point clouds. 相似文献
9.
基于激光扫描仪的点云模型的自动拼接 总被引：14，自引：0，他引：14
利用非接触式三维激光扫描仪对目标进行旋转扫描,获得目标的不同旋转位置的空间数据和数字影像,对相邻的两幅立体影像利用匹配方法寻找同名点,并映射到空间方,同时利用相对定向和空间相似变换分别剔除像方和空间方的粗差,以获得相邻两个坐标系准确的空间相似变换参数,根据空间相似变换参数将位于相邻的两个坐标系的空间点统一到同一个坐标系中去,完成三维数据的配准。 相似文献
10.
A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs
Photogrammetric methods for dense 3D surface reconstruction are increasingly available to both professional and amateur users who have requirements that span a wide variety of applications. One of the key concerns in choosing an appropriate method is to understand the achievable accuracy and how choices made within the workflow can alter that outcome. In this paper we consider accuracy in two components: the ability to generate a correctly scaled 3D model; and the ability to automatically deliver a high quality data set that provides good agreement to a reference surface. The determination of scale information is particularly important, since a network of images usually only provides angle measurements and thus leads to unscaled geometry. A solution is the introduction of known distances in object space, such as base lines between camera stations or distances between control points. In order to avoid using known object distances, the method presented in this paper exploits a calibrated stereo camera utilizing the calibrated base line information from the camera pair as an observational based geometric constraint. The method provides distance information throughout the object volume by orbiting the object.In order to test the performance of this approach, four topical surface matching methods have been investigated to determine their ability to produce accurate, dense point clouds. The methods include two versions of Semi-Global Matching as well as MicMac and Patch-based Multi-View Stereo (PMVS). These methods are implemented on a set of stereo images captured from four carefully selected objects by using (1) an off-the-shelf low cost 3D camera and (2) a pair of Nikon D700 DSLR cameras rigidly mounted in close proximity to each other. Inter-comparisons demonstrate the subtle differences between each of these permutations. The point clouds are also compared to a dataset obtained with a Nikon MMD laser scanner. Finally, the established process of achieving accurate point clouds from images and known object space distances are compared with the presented strategies.Results from the matching demonstrate that if a good imaging network is provided, using a stereo camera and bundle adjustment with geometric constraints can effectively resolve the scale. Among the strategies for dense 3D reconstruction, using the presented method for solving the scale problem and PMVS on the images captured with two DSLR cameras resulted in a dense point cloud as accurate as the Nikon laser scanner dataset. 相似文献
11.
Volkan Yilmaz Berkant Konakoglu Cigdem Serifoglu Oguz Gungor Ertan Gökalp 《国际地球制图》2018,33(3):310-320
With the advent of unmanned aerial vehicles (UAVs) for mapping applications, it is possible to generate 3D dense point clouds using stereo images. This technology, however, has some disadvantages when compared to Light Detection and Ranging (LiDAR) system. Unlike LiDAR, digital cameras mounted on UAVs are incapable of viewing beneath the canopy, which leads to sparse points on the bare earth surface. In such cases, it is more challenging to remove points belonging to above-ground objects using ground filtering algorithms generated especially for LiDAR data. To tackle this problem, a methodology employing supervised image classification for filtering 3D point clouds is proposed in this study. A classified image is overlapped with the point cloud to determine the ground points to be used for digital elevation model (DEM) generation. Quantitative evaluation results showed that filtering the point cloud with this methodology has a good potential for high-resolution DEM generation. 相似文献
12.
Automatic 3D point cloud registration is a main issue in computer vision and remote sensing. One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, assuming good a priori alignment is provided. A large body of literature has proposed many variations in order to improve each step of the process (namely selecting, matching, rejecting, weighting and minimizing). The aim of this paper is to demonstrate how the knowledge of the shape that best fits the local geometry of each 3D point neighborhood can improve the speed and the accuracy of each of these steps. First we present the geometrical features that form the basis of this work. These low-level attributes indeed describe the neighborhood shape around each 3D point. They allow to retrieve the optimal size to analyze the neighborhoods at various scales as well as the privileged local dimension (linear, planar, or volumetric). Several variations of each step of the ICP process are then proposed and analyzed by introducing these features. Such variants are compared on real datasets with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Therefore, the method is successfully applied to various 3D lidar point clouds from airborne, terrestrial, and mobile mapping systems. Improvement for two ICP steps has been noted, and we conclude that our features may not be relevant for very dissimilar object samplings. 相似文献
13.
提出了一种低空无人机(unmanned aerial vehicle,UAV)序列影像与激光点云自动配准的方法。首先分别基于多标记点过程与局部显著区域检测对激光点云和序列影像的建筑物顶部轮廓进行提取,并依据反投影临近性匹配提取的顶面特征。然后利用匹配的建筑物角点对,线性解算序列影像外方位元素,再使用建筑物边线对的共面条件进行条件平差获得优化解。最后,为消除错误提取与匹配特征对整体配准结果的影响,使用多视立体密集匹配点集与激光点集进行带相对运动阈值约束的ICP(迭代最临近点)计算,整体优化序列影像外方位元素解。试验结果表明本文方法能实现低空序列影像与激光点云像素级精度的自动配准,联合制作DOM精度满足现行无人机产品1∶500比例尺标准。 相似文献
14.
In this paper we present an approach for automatic analysis of urban accessibility using 3D point clouds. Our approach is based on range images and it consists in two main steps: urban objects segmentation and curbs detection. Both of them are required for accessibility diagnosis and itinerary planning.Our method automatically segments facades and urban objects using two hypotheses: facades are the highest vertical structures in the scene and objects are bumps on the ground on the range image. The segmentation result is used to build an urban obstacle map. After that, the gradient is computed on the ground range image. Curb candidates are selected using height and geodesic features. Then, nearby curbs are reconnected using Bézier curves. Finally, accessibility is defined based on geometrical features and accessibility standards.Our methodology is tested on two MLS databases from Paris (France) and Enschede (The Netherlands). Our experiments show that our method has good detection rates, is fast and presents few false alarms. Our method outperforms other works reported in the literature on the same databases. 相似文献
15.
由于激光点云离散分布于三维空间,导致特征计算困难,目前针对地面激光扫描点云的直线提取方法普遍存在计算量大、提取错误、或适应性不足等问题。本文提出了一种基于球面投影的单站地面激光点云直线段提取方法:首先将三维点云投影到球面上,球面投影后的三维点云保持了正确的邻接关系,同时也解决了三维数据计算量大的问题;然后,利用球面霍夫变换,正确提取球面投影后点云中的直线段特征;最后,利用稳健的直线拟合算法计算点云中的三维直线段参数。通过对室内室外场景数据的直线段提取试验及与商业软件Geomagic Studio等方法的对比试验,验证了本方法的正确性、适用性和准确性。 相似文献
16.
提出一种车载移动测量系统（MMS）激光点云与序列全景影像自动配准方法。首先采用层次化城市场景目标提取方法自激光点云提取天际线矢量,在全景影像中经虚拟成像与分割角点提取算法生成天际线矢量。然后,将提取结果作为几何配准基元,构建配准基元图,通过最小化配准基元图编辑距离进行匹配,组成共轭配准基元对,解算2D-3D粗配准模型,获得全景影像与LiDAR点云参考坐标系之间的初始转换关系。最后,为消除几何配准基元提取与匹配误差对配准结果的影响,自序列全景影像虚拟成像影像生成多视立体密集匹配点云,继而使用变种ICP算法优化其与激光点云数据间3D-3D配准参数,间接优化全景影像与激光点云间的配准参数,精化配准结果。试验结果表明,本文提出的自动配准方法可以实现车载MMS激光点云与序列全景影像的1.5像素级自动配准,配准成果可应用于真彩色点云生成等点云/影像数据融合应用。 相似文献
17.
针对现有三维点云模型重建对象化和结构化信息缺失的问题,提出一种基于图模型的二维图像语义到三维点云语义传递的算法。该算法利用扩展全卷积神经网络提取2D图像的室内空间布局和对象语义,基于以2D图像超像素和3D点云为结点构建融合图像间一致性和图像内一致性的图模型,实现2D语义到3D语义的传递。基于点云分类实验的结果表明,该方法能够得到精度较高的室内三维点云语义分类结果,点云分类的精度可达到73.875 2%,且分类效果较好。 相似文献
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卫星影像可以低成本、高频率地提供地物光谱特性观测信息,而激光点云可以提供精细的几何结构,两类数据的融合可以实现优势互补,进一步提高地物分类和信息提取的精度和自动化程度。实现亚像素级精度的几何配准是实现两类数据融合的前提,提出了一种基于线元素距离变换模型的快速配准方法。该方法以点云为控制源,将点云中的建筑物边缘等典型线元素通过卫星影像的初始有理多项式系数(rational polynomial coefficient, RPC)投影到像方空间,与卫星影像中的线元素进行迭代最近点配准,从而通过RPC参数精校正的方式实现几何配准。采用距离变换模型作为迭代最近点搜索的查找表,提高了运算效率;采用最新的渐进式鲁棒求解策略,能在噪声极多的情况下保证配准的鲁棒性。采用GeoEye-2、高分七号、WorldView-3等卫星影像与激光点云进行了配准实验,并分别通过人工精确量测的外业控制点和作业员内业刺的控制点作为检查,证明所提方法能在3种影像上达到0.4～0.7 m的配准精度,显著优于将点云映射为二维图像然后通过多模态匹配进行配准的策略。 相似文献
20.
针对城市行道树调查中,街景影像背景环境复杂多变、行道树个体差异大,依靠目视判读费时费力的问题,该文基于车载移动测量系统采集的全景影像数据,利用深度学习算法,在快速区域卷积神经网络的目标检测方法基础上,建立适用于街景行道树检测的深度神经网络模型。模型采用基于共有显著性区域及冗余策略的行道树多示例目标候选区域选择方法,使用车载图像的几何约束进一步筛选合适的候选区域,从而实现行道树目标候选区域的统一选择,提升行道树目标的检测效果。实验结果表明,该文提出的方法能够实现多种行道树的准确自动识别与提取,进而大大降低行道树绿化调查的成本。 相似文献