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Shape distribution features for point cloud analysis - a geometric histogram approach on multiple scales

Blomley, R.; Weinmann, M.; Leitloff, J.; Jutzi, B. ORCID iD icon

Abstract (englisch):

Due to ever more efficient and accurate laser scanning technologies, the analysis of 3D point clouds has become an important task in modern photogrammetry and remote sensing. To exploit the full potential of such data for structural analysis and object detection, reliable geometric features are of crucial importance. Since multiscale approaches have proved very successful for image-based applications, efforts are currently made to apply similar approaches on 3D point clouds. In this paper we analyse common geometric covariance features, pinpointing some severe limitations regarding their performance on varying scales. Instead, we propose a different feature type based on shape distributions known from object recognition. These novel features show a very reliable performance on a wide scale range and their results in classification outnumber covariance features in all tested cases.

Volltext §
DOI: 10.5445/IR/1000070470
DOI: 10.5194/isprsannals-II-3-9-2014
Zitationen: 79
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2014
Sprache Englisch
Identifikator ISSN: 2194-9050
KITopen-ID: 1000070470
Erschienen in ISPRS annals
Verlag Copernicus Publications
Band II-3
Seiten 9–16
Bemerkung zur Veröffentlichung ISPRS Technical Commission III Symposium, Zürich, CH, September 5-7, 2014
Schlagwörter LIDAR, Point Cloud, Features, Geometric Feature Design, Multiscale, Probability Histogram
Nachgewiesen in Dimensions
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