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dc.contributor.authorTarsha Kurdi, Fayez
dc.contributor.authorAwrangjeb, Mohammad
dc.contributor.authorLiew, Alan Wee-Chung
dc.date.accessioned2020-03-02T03:38:20Z
dc.date.available2020-03-02T03:38:20Z
dc.date.issued2019
dc.identifier.isbn9781728138572
dc.identifier.doi10.1109/dicta47822.2019.8946008
dc.identifier.urihttp://hdl.handle.net/10072/392035
dc.description.abstractAlthough much effort has been spent in developing a stable algorithm for 3D building modelling from Lidar data, this topic still attracts a lot of attention in the literature. A key task of this problem is the automatic building roof segmentation. Due to the great diversity of building typology, and the noisiness and heterogeneity of point cloud data, the building roof segmentation result needs to be verified/rectified with some geometric constrains before it is used to generate the 3D building models. Otherwise, the generated building model may suffer from undesirable deformations. This paper suggests the generation of 3D building model from Lidar data in two steps. The first step is the automatic 2D building modelling and the second step is the automatic conversion of a 2D building model into 3D model. This approach allows the 2D building model to be refined before starting the 3D building model generation. Furthermore, this approach allows getting the 2D and 3D building models simultaneously. The first step of the proposed algorithm is the generation of the 2D building model. Then after enhancing and fitting the roof planes, the roof plane boundaries are converted into 3D by analysing the relationships between neighbouring planes. This is followed by the adjustment of the 3D roof vertices. Experiment indicated that the proposed algorithm is accurate and robust in generating 3D building models from Lidar data.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencenameDigital Image Computing: Techniques and Applications (DICTA 2019)
dc.relation.ispartofconferencetitle2019 Digital Image Computing: Techniques and Applications (DICTA)
dc.relation.ispartofdatefrom2019-12-02
dc.relation.ispartofdateto2019-12-04
dc.relation.ispartoflocationPerth, Australia
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleAutomated Building Footprint and 3D Building Model Generation from Lidar Point Cloud Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationTarsha Kurdi, F; Awrangjeb, M; Liew, AW-C, Automated Building Footprint and 3D Building Model Generation from Lidar Point Cloud Data, 2019 Digital Image Computing: Techniques and Applications (DICTA), 2019
dc.date.updated2020-02-28T05:58:42Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorAwrangjeb, Mohammad
gro.griffith.authorTarsha Kurdi, Fayez
gro.griffith.authorLiew, Alan Wee-Chung


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