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http://dx.doi.org/10.7848/ksgpc.2016.34.5.479

Key Point Extraction from LiDAR Data for 3D Modeling  

Lee, Dae Geon (Department of Geoinformation Engineering, Sejong University)
Lee, Dong-Cheon (Department of Geoinformation Engineering, Sejong University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.34, no.5, 2016 , pp. 479-493 More about this Journal
Abstract
LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.
Keywords
LiDAR; Key Point; Corner Detection; 3D Object Modeling; Accuracy Assessment;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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