A shape-based segmentation method for mobile laser scanning point clouds |
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| Institution: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2. Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China;1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;3. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210093, China;4. State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210093, China;1. Urban Modelling Group, School of Civil, Structural and Environmental Engineering, University College Dublin, Ireland;2. Earth Institute, University College Dublin, Ireland;3. U3D Printing Hub, University College Dublin, Ireland;4. School of Computer Science and Informatics, University College Dublin, Ireland;1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;2. Collaborative Innovation Center of Geospatial Technology, Wuhan University, China |
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| Abstract: | Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well. |
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| Keywords: | Point classification Object segmentation Mobile laser scanning Object extraction |
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