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Research on extraction of point cloud data surface and line based on shared neighbor clustering algorithm

XIN Qunrong;YAO Jili;XU Guangpeng;Shandong University of Technology;  
The information of building surface and ridge line need to be quickly identified when using the laser point clouds data for building modeling or vector information extraction.A fast extraction method for building surface and ridge line based onShared Neighbor Clustering algorithm is proposed in this paper.Firstly,the unit normal vector of each data point in point cloud and the distance between point and the reference plane are calculated,and the Shared Neighbor Clustering algorithm based on the grid is used to classify the point cloud to determine the building surface point cloud.Then,the intersection plane is automatically judged and the building edges are extracted,and the extraction results of a building surface based on RANSAC algorithm are compared with the extraction results of the building edges.The results show that the method has high degree of automation,the extraction of building surface and ridge line is fast and accurate,and the extraction results can be applied to 3 dbuilding automatic modeling and mapping
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