Abstract
Terrestrial Laser Scanning has been established as a leading tools to collect dense point cloud over object surface. The collected point cloud does not provide semantic information about the scanned object. Therefore, different methods have been developed to deal with this problem, it may be the most effective one to segment point cloud into basic primitives. This paper intrudes a modified method based on RANSAC to identify planar, cylindrical and spherical surfaces in point cloud. The method firstly construct space division by 3D grid, draw a random sample to determine a sub-cell and carry local-RANSAC method to detect multi-primitive models in it, and get the candidate model(s) by local score, then the best model in the candidate modelscan be obtained by statistic inference, and its consensus set determined by distance and normal vector constrains in the global range. Finally the experimental results show that our method can segment man-made objects with regular geometry shape efficiently, and deal with the over and under segmentation properly.
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Wang, Y., Shi, H. (2015). A Segmentation Method for Point Cloud Based on Local Sample and Statistic Inference. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2014. Communications in Computer and Information Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45737-5_28
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DOI: https://doi.org/10.1007/978-3-662-45737-5_28
Publisher Name: Springer, Berlin, Heidelberg
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