Semiautomatic Construction of 2-D Fçade Footprints From Mobile LiDAR Data

Although mobile light detection and ranging (LiDAR) technology has excellent potential in mapping street scenes, there is little research in constructing fçade footprints from unorganized, uneven, and incomplete mobile LiDAR point clouds. In fact, fçade footprint vectorization from mobile LiDAR data...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2019-01, Vol.57 (6), p.4005
Main Authors: Xia, Shaobo, Wang, Ruisheng
Format: Article
Language:English
Subjects:
Data
Datasets
Detection
Footprints
Frameworks
Ground truth
Lidar
Line intersections
Mapping
Residential buildings
Three dimensional models
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Summary:Although mobile light detection and ranging (LiDAR) technology has excellent potential in mapping street scenes, there is little research in constructing fçade footprints from unorganized, uneven, and incomplete mobile LiDAR point clouds. In fact, fçade footprint vectorization from mobile LiDAR data still involves a lot of manual work, especially in complex street scenes with various types of buildings. In this paper, we present a new and effective framework for extracting 2-D fçade footprints from mobile LiDAR point clouds. The proposed framework consists of three steps: 1) line segment extraction from projected point clouds based on a hypotheses and selection strategy; 2) completion of missing parts between adjacent walls using line intersections; and 3) delineation of footprints through finding the least cost path in the graph of the line segments. We compare our method with several existing ones and discuss its robustness against data missing and noise such as nonwall structures and vegetation. Our proposed method is also tested in two large-scale data sets, a residential data set, and an urban data set. The coverage ratio, i.e., the percentage of outer wall points covered by the generated outlines in the residential data set is 93.4% and 91.7% in the urban data set is achieved. The mean distance between points of ground truth and constructed footprints for the residential data set and urban data set is 0.019 and 0.028 m, respectively. The experimental results demonstrate that the proposed framework is effective in modeling various fçade footprints from mobile LiDAR point clouds.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2018.2889335