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Automatic Segmentation of Urban Point Clouds Based on the Gaussian Map
Author(s) -
Wang Yinghui,
Hao Wen,
Ning Xiaojuan,
Zhao Minghua,
Zhang Jiulong,
Shi Zhenghao,
Zhang Xiaopeng
Publication year - 2013
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12041
Subject(s) - point cloud , segmentation , cluster analysis , gaussian , artificial intelligence , computer science , point (geometry) , computer vision , region growing , process (computing) , image segmentation , pattern recognition (psychology) , scale space segmentation , mathematics , geometry , physics , quantum mechanics , operating system
A comprehensive method to segment urban point clouds based on the Gaussian map is presented. The normals of point clouds are firstly mapped to the Gaussian sphere and then partitioned into groups using a mean shift clustering algorithm. Next, a distance‐based clustering method is presented to tackle overlapping surfaces which avoids under‐segmentation. Based on the properties of the Gaussian map and the geometric information of the points, primitive shapes such as planes, cylinders, cones and spheres are recognised. Trees, cars, street lights and other objects are then segmented by using the distance‐based clustering method after removing the planes, cylinders, cones and spheres from the large urban scenes. Finally, a refinement process based on primitive shapes is implemented to improve the segmentation results which effectively avoids over‐segmentation. Experimental results demonstrate that the proposed method can be used as a robust way to segment urban point clouds based on the Gaussian map.