Title
Hierarchical Regularization of Building Boundaries in Noisy Aerial Laser Scanning and Photogrammetric Point Clouds.
Abstract
Aerial laser scanning or photogrammetric point clouds are often noisy at building boundaries. In order to produce regularized polygons from such noisy point clouds, this study proposes a hierarchical regularization method for the boundary points. Beginning with detected planar structures from raw point clouds, two stages of regularization are employed. In the first stage, the boundary points of an individual plane are consolidated locally by shifting them along their refined normal vector to resist noise, and then grouped into piecewise smooth segments. In the second stage, global regularities among different segments from different planes are softly enforced through a labeling process, in which the same label represents parallel or orthogonal segments. This is formulated as a Markov random field and solved efficiently via graph cut. The performance of the proposed method is evaluated for extracting 2D footprints and 3D polygons of buildings in metropolitan area. The results reveal that the proposed method is superior to the state-of-art methods both qualitatively and quantitatively in compactness. The simplified polygons could fit the original boundary points with an average residuals of 0.2 m, and in the meantime reduce up to 90% complexities of the edges. The satisfactory performances of the proposed method show a promising potential for 3D reconstruction of polygonal models from noisy point clouds.
Year
DOI
Venue
2018
10.3390/rs10121996
REMOTE SENSING
Keywords
Field
DocType
point clouds,boundary extraction,regularization,building reconstruction
Computer vision,Photogrammetry,Laser scanning,Remote sensing,Regularization (mathematics),Artificial intelligence,Geology,Point cloud
Journal
Volume
Issue
ISSN
10
12
2072-4292
Citations 
PageRank 
References 
0
0.34
22
Authors
7
Name
Order
Citations
PageRank
Linfu Xie100.34
Qing Zhu214631.03
Han Hu362.21
Bo Wu46920.31
Yuan Li500.34
Yeting Zhang6378.36
Ruofei Zhong74518.76