TY - JOUR
T1 - Three-dimensional building façade segmentation and opening area detection from point clouds
AU - Zolanvari, S. M.Iman
AU - Laefer, Debra F.
AU - Natanzi, Atteyeh S.
N1 - Funding Information:
This work was sponsored by European Research Council grant ERC-2012-StG_20111012 “RETURN – Rethinking Tunnelling in Urban Neighbourhoods” Project 30786. The authors thank Anh-Vu Vo for applying the Octree-based region-growing ( Vo et al., 2015 ) on Cases A and B in the discussion.
Publisher Copyright:
© 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
PY - 2018/9
Y1 - 2018/9
N2 - Laser scanning generates a point cloud from which geometries can be extracted, but most methods struggle to do this automatically, especially for the entirety of an architecturally complex building (as opposed to that of a single façade). To address this issue, this paper introduces the Improved Slicing Method (ISM), an innovative and computationally-efficient method for three-dimensional building segmentation. The method is also able to detect opening boundaries even on roofs (e.g. chimneys), as well as a building's overall outer boundaries using a local density analysis technique. The proposed procedure is validated by its application to two architecturally complex, historic brick buildings. Accuracies of at least 86% were achieved, with computational times as little as 0.53 s for detecting features from a data set of 5.0 million points. The accuracy more than rivalled the current state of the art, while being up to six times faster and with the further advantage of requiring no manual intervention or reliance on a priori information.
AB - Laser scanning generates a point cloud from which geometries can be extracted, but most methods struggle to do this automatically, especially for the entirety of an architecturally complex building (as opposed to that of a single façade). To address this issue, this paper introduces the Improved Slicing Method (ISM), an innovative and computationally-efficient method for three-dimensional building segmentation. The method is also able to detect opening boundaries even on roofs (e.g. chimneys), as well as a building's overall outer boundaries using a local density analysis technique. The proposed procedure is validated by its application to two architecturally complex, historic brick buildings. Accuracies of at least 86% were achieved, with computational times as little as 0.53 s for detecting features from a data set of 5.0 million points. The accuracy more than rivalled the current state of the art, while being up to six times faster and with the further advantage of requiring no manual intervention or reliance on a priori information.
KW - Feature detection
KW - Laser scanning
KW - Light Detection and Ranging (LiDAR)
KW - Point cloud segmentation
KW - Three-dimensional model reconstruction
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U2 - 10.1016/j.isprsjprs.2018.04.004
DO - 10.1016/j.isprsjprs.2018.04.004
M3 - Article
AN - SCOPUS:85046760564
SN - 0924-2716
VL - 143
SP - 134
EP - 149
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
ER -