Modeling Façade Structures Using Point Clouds From Dense Image Matching
Modeling Façade Structures Using Point Clouds From Dense Image Matching
复制标题
使用密集图像匹配的点云对立面结构进行建模
DOI：
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复制
发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. Rothermel
中科院分区：
文献类型：
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作者：
D. Fritsch;S. Becker;M. Rothermel
为城市规划和灾害管理自动生成三维计算机模型是一项持续进行了约20年的活动。10-15年来，航空激光雷达似乎是解决这一目的的数据收集问题的关键技术。今天，具有最低点和倾斜观测方向的航空照片克服了许多障碍，并且可以通过密集图像匹配算法进行处理，确实提供了比激光雷达点云更密集的点云。对于某些应用来说，地面图像似乎是一种低成本的数据收集方法，并且可以提供非常密集的点云进行进一步处理。本文主要研究了基于密集图像匹配算法的地面图像数据采集与处理。为了解决点云建模问题，一个形式化的语法处理管道提供了结构化的LoD3 （Level-of-Detail）建筑信息。这些信息可能有助于改进现有的土木工程有限元软件，以考虑在某些地下活动中需要监测的关键建筑部分。此外，LoD3模型将通过先进的隔热和噪音过滤措施，帮助提高城市建筑的舒适度。
The automated generation of 3D computer models for urban planning and disaster management is an ongoing activity since about two decades. For 10-15 years aerial LiDAR seemed to be a key technology to solve the data collection problem for this purpose. Today, aerial photographs with nadir and oblique viewing directions overcome many obstacles and can be processed by dense image matching algorithms do deliver very dense point clouds outnumbering LiDAR point clouds. For some applications terrestrial images seem to be a low-cost data collection method delivering as well very dense point clouds for further processing. This paper is focused on terrestrial image data collection and its processing using dense image matching algorithms. To solve the point cloud modeling problem a formal grammar processing pipeline delivers structured building information of Level-of-Detail 3 (LoD3). This information might contribute to refine existing FEM software of Civil Engineering to consider critical building parts, to be monitored during some underground activities. Furthermore, the LoD3 model will help to increase building comfort in cities by adavanced insulation and noise filter measures.