| 基于车载激光点云的街景立面自动提取 |
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| 引用本文： | 耿雨馨,钟若飞,彭宝江.基于车载激光点云的街景立面自动提取[J].地球信息科学,2018,20(4):480-488. |
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| 作者姓名： | 耿雨馨 钟若飞 彭宝江 |
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| 作者单位： | 1. 首都师范大 学资源环境与旅游学院,北京 1000482. 首都师范大学 北京成像技术高精尖创新中心,北京 1000483. 首都师范大学 三维数据获取与应用重点实验室,北京 100048 |
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| 基金项目： | 国家自然科学基金项目(41371434) |
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| 摘 要： | 街道景观图是城市规划设计和城市管理的重要参考依据,车载点云数据能够提供沿街建筑的三维点信息,精度高,覆盖范围广泛,为街景立面整治提供了新的解决方案。为此,本文提出一种适用于车载点云的街景立面的自动提取方法,提取立面点云的具体步骤为：对原始数据去噪滤波;选取非地面点构建规则格网并二值化,依据语义特征筛选出建筑物点云;用POS数据拟合直线段帮助选取参考向量与参考平面;计算点云到参考面的距离,按距离分类点云数据,并对前述步骤中未分类点另行提取,合并面点集得到以沿街建筑物立面为主的街景立面点云。为了验证这一方法的可行性和有效性,采用点云数据进行实验,实验结果表明本方法在一定程度上提高了数据处理效率,能得到较理想的结果。
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| 关 键 词： | 立面提取 车载点云 街景立面 POS轨迹 法向量 |
| 收稿时间： | 2017-09-21 |
Automatic Extraction of Vista Facades Based on Vehicle-borne Laser Point Cloud |
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| GENG Yuxin,ZHONG Ruofei,PENG Baojiang.Automatic Extraction of Vista Facades Based on Vehicle-borne Laser Point Cloud[J].Geo-information Science,2018,20(4):480-488. |
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| Authors: | GENG Yuxin ZHONG Ruofei PENG Baojiang |
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| Institution: | 1. College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China2. Beijing Advanced Innovation Center for Imaging Technology,Capital Normal University, Beijing 100048, China3. Key Lab of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China |
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| Abstract: | The street landscape describes the view of buildings and other objects on both sides of the road and works as the window of a city′s overall image. It is quite vital for urban planning and design, which could be helpful reference for management of government. Vehicle-borne point cloud data, with high precision and wide coverage, can provide position information and shape characteristics of the buildings along streets. This makes it possible to provide a new solution for urban vista facades extraction. Based on facades management, we propose a novel approach for automatic extraction of vista facades from vehicle-borne laser scanning data. The detail introduction focuses on the extraction of facades. In the approach, we divided the ground points and non-ground points after de-noising raw data and separated buildings from non-ground points in order to extract vista facades. It works in following four steps: (1) denoise raw point cloud and remove surface feature points from the raw data in order to acquire points of objects on the ground; (2) construct regular grids for non-ground points with binarization processing and select points of building according to semantic features; (3) estimate the reference vectors via POS(Positioning and Orientation System) data and set those vectors as normal vectors of chosen reference planes; (4) compute the Euclidean distance between each point and each plane. Points are classified by the distances with the same plane, based on which we extract point cloud of vista facades. To verify the feasibility and effectiveness of this method, we used a large group of vehicle-borne laser point cloud to carry out a series of experiments, including separating buildings from ground in origin data,extracting facade points from building points and comparing the automatic extraction with manual selection and results of other methods. The results showed that the method could improve the efficiency of data processing to some extent and return good results. The superiority of it was also verified by experiments. |
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| Keywords: | facade extraction vehicle-borne point cloud vista facades POS data normal vector |
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