LIDAR点云中多层屋顶轮廓线提取方法研究

在分析LIDAR数据提取建筑物轮廓线现状的基础上,针对多层、非规则屋顶轮廓线提取的难点,提出一种直接基于离散点云的屋顶轮廓线提取方法,该方法主要包括屋顶点的识别,初始轮廓线的提取以及轮廓线的规则化等步骤。最后采用实地数据进行验证,结果表明该方法具有一定的应用前景。     

Robust Segmentation of Multiple Intersecting Manifolds from Unoriented Noisy Point Clouds

We present a method for extracting complex manifolds with an arbitrary number of (self‐) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three‐step process. First, small flat neighbourhoods of all possible orientations are created around all points. Next, neighbourhoods are assembled into larger quasi‐flat patches, whose overlaps give the global connectivity structure of the point cloud. Finally, curved manifolds are extracted from the patch connectivity graph via a multiple‐source flood fill. The manifolds can be reconstructed into meshed surfaces using standard existing surface reconstruction methods. We demonstrate the speed and robustness of our method on several point clouds, with applications in point cloud segmentation, denoising and medial surface reconstruction.     

Extracting lines of curvature from noisy point clouds

We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.     

结合全局与局部信息的点云目标识别模型库构建

目的 点云目标识别流程分为离线与在线阶段。离线阶段基于待识别目标的CAD模型构建一个模型库,在线基于近邻查找完成识别。本文针对离线阶段,提出一种新的模型库构建方法。方法 首先将CAD模型置于一个二十面体中心,使用多个虚拟相机获取CAD模型在不同视角下的点云;然后将每个不同视角下的点云进行主成分分析并基于主成分分析的结果从多个选定的方向将点云切分为多个子部分,这些子部分包含点云的全局及局部信息;接着对每个子部分使用聚类算法获取其最大聚类,去除离群点;最后结合多种方式删减一些冗余聚类,减小模型库规模。结果 在多个公开数据集上使用多种点云描述子进行对比实验,识别结果表明,相对于传统的模型库构建方法,基于本文方法进行识别正确率更高,在某些点云描述子上的识别正确率提升达到10%以上。结论 通过将CAD模型在不同视角下点云的全局与局部信息都加入模型库中,本文提出的模型库构建方法可有效提高点云目标识别正确率,改善了场景目标发生遮挡时,近邻查找识别精度不高的问题。     

End-to-end point cloud-based segmentation of building members for automating dimensional quality control

A frequent and accurate quality inspection procedure to assess the quality requirements during the life cycle of buildings is crucial. Among different quality measures, the dimensional quality that involves spatial features of buildings is of significant importance. However, the traditional manual inspection of dimensional quality in buildings is unreliable and tedious. Thus, this study presents an end-to-end method for quality inspection of building structural members using point cloud datasets. The proposed method, first, detects and labels structural members within the point cloud based on a set of domain-specific geometric and semantic definitions. Then, each structural member's section width, height, and length are obtained with the proposed bounding box method. Experiments on three real-world buildings' point clouds with various geometric features and noise levels, occlusion, and outliers were also conducted, illustrating the performance efficiency and accuracy of the proposed model for dimensional quality inspection of building structural members.     

Study on Power Tower Extraction and Fast Positioning from Airborne LiDAR Data

Airborne LiDAR has become an important technique for transmission line digitalization,reconstruction and safety inspection.Moreover,accurately and efficiently extracting the position of each tower from massive point clouds is basic and important task for the applications in power industry.In this study,a method was proposed to efficiently extract the point clouds and fast determine the position of power towers using airborne LiDAR data.Firstly,the point clouds of power towers were automatically separated from raw data based on the spatial distribution characteristics of airborne LiDAR data.Secondly,each power tower was efficiently detected using a region\|growing algorithm.Finally,a least square linear fitting method was used to determine the accurate position of each power tower.The new proposed method was applied to several LiDAR data sets in areas with high voltage transmission lines.Results indicated that the integrity of the power towers’ points is up to 91.1%,and the accuracy of center positions is high enough with the medium error of 13.5 cm.Additionally,our study also concluded that the proposed method is robust and applicable even the point density is relatively low.     

基于点重要性判断的点云简化

为了有效保持散乱点云的显著几何特征,提高点云简化的精度和效率,提出一种点重要性判断点云简化方法.首先,计算点云中点的重要性,并根据重要性提取特征点;然后,采用八叉树算法对非特征点进行简化,从而保留点云的主要细节特征,实现点云简化处理;最后,通过对公共点云和文物点云数据模型的简化实验来验证该点云简化方法.结果表明,该点重要性判断点云简化方法可以在有效保持点云细节几何特征的同时,实现点云的有效简化,是一种快速、高精度的点云简化方法.     

结合超体素和区域增长的植物器官点云分割

点云分割是点云识别与建模的基础。为提高点云分割准确率和效率，提出一种结合超体素和区域增长的自适应分割算法。根据三维点云的空间位置和法向量信息，利用八叉树对点云进行初始分割得到超体素。选取超体素的中心体素组成一个新的重采样后的密度均匀点云，降低原始点云数据处理量，从而减少运算时间。建立重采样后点云数据的K-D树索引，根据其局部特征得到点云簇。最后将聚类结果返回到原始点云空间。分别选取植物三个物候期的激光扫描点云，对该方法的有效性进行验证。实验结果表明，该方法分割后点云与手工分割平均拟合度达到93.38%，高于其他同类方法，且算法效率得到明显提升。     

Automated extraction of street-scene objects from mobile lidar point clouds

Mobile laser scanning or lidar is a new and rapid system to capture high-density three-dimensional (3-D) point clouds. Automatic data segmentation and feature extraction are the key steps for accurate identification and 3-D reconstruction of street-scene objects (e.g. buildings and trees). This article presents a novel method for automated extraction of street-scene objects from mobile lidar point clouds. The proposed method first uses planar division to sort points into different grids, then calculates the weights of points in each grid according to the spatial distribution of mobile lidar points and generates the geo-referenced feature image of the point clouds using the inverse-distance-weighted interpolation method. Finally, the proposed method transforms the extraction of street-scene objects from 3-D mobile lidar point clouds into the extraction of geometric features from two-dimensional (2-D) imagery space, thus simplifying the automated object extraction process. Experimental results show that the proposed method provides a promising solution for automatically extracting street-scene objects from mobile lidar point clouds.     

基于毫米波雷达稀疏点云的人体行为识别方法

目前利用毫米波雷达进行人体行为识别的方法在复杂场景下无法很好的区分相似动作,与此同时模型的鲁棒性和抗干扰能力也相对较差;针对以上两个问题,提出了一种通用的基于毫米波雷达稀疏点云的人体行为识别方法,该方法首先利用K-means++聚类算法对点云进行采样,然后使用基于注意力特征融合的点云活动分类网络进行人体行为特征的提取和识别,该网络可以兼顾点云的空间特征以及时序特征,对稀疏点云的运动有灵敏的感知能力;为了验证所提出方法的有效性和鲁棒性,分别在MMActivity数据集和MMGesture数据集上进行了实验,其在两个数据集上取得97.50%和94.10%的准确率,均优于其它方法;此外,进一步验证了K-means++点云采样方法的有效性,相较于随机采样,准确率提升了0.4个百分点,实验结果表明所提出方法能够有效的提升人体行为识别的准确率,且模型具有较好的泛化能力。     

可变半径Alpha Shapes提取机载LiDAR点云建筑物轮廓

目的 机载激光雷达（light detection and ranging,LiDAR）能够快速获取建筑物表面的3维点云,为提取建筑物轮廓提供重要的数据支撑,但由于激光脚点的随机性和点云自身的离散性,常规固定半径Alpha Shapes（A-Shapes）算法难以兼顾轮廓提取的精细度和完整度,且在点数量较大情况下计算效率较低。因此,提出一种基于网格的可变半径Alpha Shapes方法用于提取机载LiDAR点云建筑物轮廓。方法 对3维点云进行投影降维,对投影后2维离散点的范围构建规则格网,接着根据网格内点云填充情况筛选出边界网格,计算边界网格的平滑度并加权不同的滚动圆半径,再以边界网格为中心生成3×3邻域网格检测窗口,利用滚动圆原理提取窗口内点集的边界点,迭代检测直到所有边界网格遍历完成,最后获取点云的完整轮廓。结果 在精度评价实验中,与固定半径A-Shapes方法和可变半径Alpha Shapes（variable radius Alpha Shapes,VA-Shapes）方法相比,若建筑物以直线特征为主且边缘点云参差不齐,则本文方法的提取效果不理想;若建筑物含有较多拐角特征,则本文方法的提取效果较好。在效率评价实验中,与A-Shapes方法、VA-Shapse方法以及包裹圆方法相比,若点云数据量较小,则4种方法的耗时差距不大;若数据量较大,则本文方法和包裹圆方法的耗时远小于固定半径A-Shapes方法。实验结果表明,本文提出的轮廓提取方法适用于多种形状的建筑物点云。从轮廓完整性、几何精度以及计算效率等几方面综合考虑,本文方法提取建筑物点云轮廓效果较好。结论 本文提出的基于网格的可变半径Alpha Shapes建筑物点云轮廓提取方法结合了网格划分和滚动圆检测的优点,能够有效提取机载LiDAR建筑物点云顶部轮廓,具有较高的提取效率和良好的鲁棒性,提取的轮廓精度较高。     

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.     

基于边缘关联点云的激光雷达与相机外参标定方法

激光雷达的点云和相机的图像经常被融合应用在多个领域。准确的外参标定是融合两者信息的前提。点云特征提取是外参标定的关键步骤。但是点云的低分辨率和低质量会影响标定结果的精度。针对这些问题,提出一种基于边缘关联点云的激光雷达与相机外参标定方法。首先,利用双回波提取标定板边缘关联点云;然后,通过优化方法从边缘关联点云中提取出与实际标定板尺寸大小兼容的标定板角点;最后,将点云中角点和图像中角点匹配。用多点透视方法求解激光雷达与相机之间的外参。实验结果表明,该方法的重投影误差为1.602px,低于同类对比方法,验证了该方法的有效性与准确性。     

基于背包激光扫描点云强度的胸径提取方法研究

为了提高背包激光扫描点云林木胸径提取精度。以3块山区人工林做为研究对象,选取距离地面1.3 m处一定厚度的树干点云为胸径切片,切片厚度分别为0.2 m、0.4 m和0.6 m。对切片点云基于点云强度划分点云区间以获得多种胸径切片,将处理得到的切片点云映射到二维平面,采用最小二乘法对二维点进行胸径提取。结果表明：切片厚度为0.6 m、强度区间为［5,10］的切片点云提取胸径结果最好,3块样地胸径提取结果RMSE分别为0.46 cm、0.83 cm和1.03 cm,MAE分别为0.37 cm、0.66 cm和0.81 cm,相对精度分别为97.03%、94.73%和96.73%。相比于同等条件下完整点云结果,RMSE分别降低了61.34%、25.90%和61.71%,MAE分别降低了68.91%、31.96%和65.97%,相对精度分别提高了6.10%、1.95%和5.8%。并且使用点云数量分别降低了97.63%、97.25%、97.83%,平均用时分别提高了98.5%、97.6%、96.36%。通过使用最佳强度区间内的点云提取胸径,不仅可以减少点云数量节约时间成本,更能够提高胸径提取精度,并为提取其他林木参数提供参考。     

保特征的点云骨架提取算法

三维模型的骨架提取是计算机图形学中一个重要的研究方向。对于有噪声的点云模型,曲线骨 架提取的难点在于保持正确的拓扑结构以及良好的中心性;对于无噪声的点云模型,曲线骨架提取的难点在于 对模型细节特征的保留。目前主流的点云骨架提取方法往往无法同时解决这 2 个难点。算法在最优传输理论的 基础之上结合聚类的思想,将点云骨架提取的问题转化为一个最优化问题。首先使用最优传输得到原始点云与 采样点云之间的传输计划。然后使用聚类的思想将原始点云进行分割,采样点即成为了簇的中心。接着通过簇 与簇之间的调整与合并减少聚类个数,优化聚类结果。最后通过迭代的方式得到粗糙的骨架并使用插点操作进 行优化。大量实验结果表明,该算法在有噪声与无噪声的三维点云模型上均能提取出质量良好的曲线骨架并保 留模型的特征。     

Context-aware sequence labeling for condition information extraction from historical bridge inspection reports

Effective upkeep of aging infrastructure systems with limited funding and resources calls for efficient bridge management systems. Although data-driven models have been extensively studied in the last decade for extracting knowledge from past experience to guide future maintenance decision making, their performance and usefulness have been limited by the level of detail and accuracy of currently available bridge condition databases. This paper leverages an untapped resource for bridge condition data and proposes a new method to extract condition information from it at a high level of detail. To that end, a natural language processing approach was developed to formalize structural condition knowledge by formulating a sequence labeling task and modeling inspection narratives as a combination of words representing defects, their severity and location, while accounting for the context of each word. The proposed framework employs a deep-learning-based approach and incorporates context-aware components including a bi-directional Long Short Term Memory (LSTM) neural network architecture and a Conditional Random Field (CRF) classifier to account for the context of words when assigning labels. A dependency-based word embedding model was also used to represent the raw text while incorporating both semantic and contextual information. The sequence labeling model was trained using bridge inspection reports collected from the Virginia Department of Transportation bridge inspection database and achieved an F1 score of 94.12% during testing. The proposed model also demonstrated improvements compared with baseline sequence labeling models, and was further used to demonstrate the capability of detecting condition changes with respect to previous inspection records. Results of this study show that the proposed method can be used to extract and create a condition information database that can further assist in developing data-driven bridge management and condition forecasting models, as well as automated bridge inspection systems.     

Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition

Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment.     

基于RANSAC的公路路面坑槽提取方法

针对基于三维激光点云的坑槽扫描提取算法计算量大、效率低的问题,提出基于RANSAC思想的坑槽提取方法.首先,使用RANSAC计算横断面基准线,矫正横断面数据并初步识别坑槽点及其位置;其次,对坑槽区域使用RANSAC计算坑槽局部基准路面,由此标记出坑槽点及路面点;然后使用种子填充算法进行连通域求解,计算出坑槽点集;最后对...     

Automatic segmentation of point clouds from multi-view reconstruction using graph-cut

In multi-view reconstruction systems, the recovered point cloud often consists of numerous unwanted background points. We propose a graph-cut based method for automatically segmenting point clouds from multi-view reconstruction. Based on the observation that the object of interest is likely to be central to the intended multi-view images, our method requires no user interaction except two roughly estimated parameters of objects covering in the central area of images. The proposed segmentation process is carried out in two steps: first, we build a weighted graph whose nodes represent points and edges that connect each point to its k-nearest neighbors. The potentials of each point being object and background are estimated according to distances between its projections in images and the corresponding image centers. The pairwise potentials between each point and its neighbors are computed according to their positions, colors and normals. Graph-cut optimization is then used to find the initial binary segmentation of object and background points. Second, to refine the initial segmentation, Gaussian mixture models (GMMs) are created from the color and density features of points in object and background classes, respectively. The potentials of each point being object and background are re-calculated based on the learned GMMs. The graph is updated and the segmentation of point clouds is improved by graph-cut optimization. The second step is iterated until convergence. Our method requires no manual labeling points and employs available information of point clouds from multi-view systems. We test the approach on real-world data generated by multi-view reconstruction systems.     

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local surface fitting (e.g. jets or MLS surfaces), local or non-local averaging or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data-driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely sampled point clouds. In our extensive evaluation, both on synthetic and real data, we show an increased robustness to strong noise levels compared to various state-of-the-art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline. Both the code and pre-trained networks can be found on the project page ( https://github.com/mrakotosaon/pointcleannet ).