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2010 Seventh Canadian Conference on Computer and Robot Vision (CRV 2010) (2010)
Ottawa, ON
May 31, 2010 to June 2, 2010
ISBN: 978-1-4244-6963-5
pp: 122-129
Karim Hammoudi , Lab. MATIS, Univ. Paris-Est, Paris, France
Fadi Dornaika , IKERBASQUE, Basque Found. for Sci., Univ. of the Basque Country, Bilbao, Spain
Bahman Soheilian , Lab. MATIS, Univ. Paris-Est, Paris, France
Nicolas Paparoditis , Lab. MATIS, Univ. Paris-Est, Paris, France
ABSTRACT
This paper presents an approach for extracting 3D outlined planar clusters of street facades. Terrestrial laser data are acquired using a Mobile Mapping System (MMS). Mapping of street facades is of great interest in various digital mapping and robotic research topics. After a filtering step of the 3D point cloud, the dominant hypothetical facade planes are detected using an adapted Progressive Probabilistic Hough Transform (PPHT). The corresponding planar clusters are extracted using a priori geometric knowledge of street. The clusters are horizontally and vertically delimited using heuristic approaches. The adapted PPHT allows the automatic extraction of georeferenced planar clusters of facades with a fine detection of dominant facade lines and a low computation time. The adopted approach has been tested on a set of point cloud acquired in the city of Paris under real conditions. Examples and experimental results show the efficiency and the potential of the proposed approach.
INDEX TERMS
Laser modes, Data mining, Cities and towns, Urban areas, Robot sensing systems, Image reconstruction, Simultaneous localization and mapping, Three-dimensional displays, Computer vision, Robot vision systems,Hough transform, 3D city modeling, 3D street reconstruction, 3D point cloud, terrestrial laser scanning
CITATION
Karim Hammoudi, Fadi Dornaika, Bahman Soheilian, Nicolas Paparoditis, "Extracting outlined planar clusters of street facades from 3D point clouds", 2010 Seventh Canadian Conference on Computer and Robot Vision (CRV 2010), vol. 00, no. , pp. 122-129, 2010, doi:10.1109/CRV.2010.23
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