Automatic generation of structural building descriptions from 3D point cloud scans
Automatic generation of structural building descriptions from 3D point cloud scans
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DOI：
10.5220/0004689601200127
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发表时间：
2015-10
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通讯作者：
Sebastian Ochmann;Richard Vock;Raoul Wessel;M. Tamke;R. Klein
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作者：
Sebastian Ochmann;Richard Vock;Raoul Wessel;M. Tamke;R. Klein
我们提出了一种新的方法，自动语义结构的三维点云表示建筑物。与现有的方法相比，这些方法要么针对外观，如立面结构，要么针对低层次的几何结构，我们专注于建筑物的内部，使用室内扫描来获得高层次的建筑实体，如房间和门。从注册的3D点云开始，我们对每个测量点与建筑物中某个房间的从属关系进行概率建模。我们使用迭代算法来解决由此产生的聚类问题，该算法依赖于点云内任何两个位置之间的估计vibration。有了手头的房间分割，我们随后确定相邻房间之间的门的位置和范围。在我们的实验中，我们证明了我们的方法的可行性，将其应用到合成以及现实世界的数据。
We present a new method for automatic semantic structuring of 3D point clouds representing buildings. In contrast to existing approaches which either target the outside appearance like the facade structure or rather low-level geometric structures, we focus on the building's interior using indoor scans to derive high-level architectural entities like rooms and doors. Starting with a registered 3D point cloud, we probabilistically model the affiliation of each measured point to a certain room in the building. We solve the resulting clustering problem using an iterative algorithm that relies on the estimated visibilities between any two locations within the point cloud. With the segmentation into rooms at hand, we subsequently determine the locations and extents of doors between adjacent rooms. In our experiments, we demonstrate the feasibility of our method by applying it to synthetic as well as to real-world data.