Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives

Song, J; Xia, S; Wang, J; Chen, D

Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives

Song, J Xia, S Wang, J Chen, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2021, 59, (2), pp. 1660-1674
Issue Date:: 2021-02-01

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Field	Value	Language
dc.contributor.author	Song, J
dc.contributor.author	Xia, S
dc.contributor.author	Wang, J
dc.contributor.author	Chen, D
dc.date.accessioned	2022-07-05T02:57:08Z
dc.date.available	2022-07-05T02:57:08Z
dc.date.issued	2021-02-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2021, 59, (2), pp. 1660-1674
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/158648
dc.description.abstract	Airborne light detection and ranging (LiDAR) data are widely applied in building reconstruction, with studies reporting success in typical buildings. However, the reconstruction of curved buildings remains an open research problem. To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives. The input LiDAR point clouds are first converted into contours where individual buildings are identified. After recognizing geometric units (primitives) from building contours, we get initial models by matching the basic geometric primitives to these primitives. To polish assembly models, we employ a warping field for model refinements. Specifically, an embedded deformation (ED) graph is constructed via downsampling the initial model. Then, the point to model displacements is minimized by adjusting node parameters in the ED graph based on our objective function. The presented framework is validated on several highly curved buildings collected by various LiDAR in different cities. The experimental results, as well as accuracy comparison, demonstrate the advantage and effectiveness of our method. The new insight attributes to an efficient reconstruction manner. Moreover, we prove that the primitive-based framework significantly reduces the data storage to 10%-20% of classical mesh models.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2020.2995732
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives
dc.type	Journal Article
utslib.citation.volume	59
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-05T02:57:04Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	59
utslib.citation.issue	2

Abstract:

Airborne light detection and ranging (LiDAR) data are widely applied in building reconstruction, with studies reporting success in typical buildings. However, the reconstruction of curved buildings remains an open research problem. To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives. The input LiDAR point clouds are first converted into contours where individual buildings are identified. After recognizing geometric units (primitives) from building contours, we get initial models by matching the basic geometric primitives to these primitives. To polish assembly models, we employ a warping field for model refinements. Specifically, an embedded deformation (ED) graph is constructed via downsampling the initial model. Then, the point to model displacements is minimized by adjusting node parameters in the ED graph based on our objective function. The presented framework is validated on several highly curved buildings collected by various LiDAR in different cities. The experimental results, as well as accuracy comparison, demonstrate the advantage and effectiveness of our method. The new insight attributes to an efficient reconstruction manner. Moreover, we prove that the primitive-based framework significantly reduces the data storage to 10%-20% of classical mesh models.

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http://hdl.handle.net/10453/158648