Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/100739
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorAhmed, Wen_US
dc.creatorShi, Wen_US
dc.creatorXu, Wen_US
dc.date.accessioned2023-08-11T03:13:07Z-
dc.date.available2023-08-11T03:13:07Z-
dc.identifier.isbn978-1-7281-0247-4 (Electronic)en_US
dc.identifier.isbn978-1-7281-0246-7 (USB)en_US
dc.identifier.isbn978-1-7281-0248-1 (Print on Demand(PoD))en_US
dc.identifier.urihttp://hdl.handle.net/10397/100739-
dc.description2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), 12-14 December 2018, Sophia Antipolis, France.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2018 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.en_US
dc.rightsThe following publication W. AHMED, W. Shi and W. XU, "Modeling Complex Building Structure (LoD2) Using Image-Based Point Cloud," 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France, 2018, pp. 110-114 is available at https://doi.org/10.1109/IPAS.2018.8708864.en_US
dc.subjectLoD2en_US
dc.subjectModel drivenen_US
dc.subjectOutdoor modelingen_US
dc.subjectRANSACen_US
dc.subjectUAVen_US
dc.titleModeling complex building structure (LoD2) using image-based point clouden_US
dc.typeConference Paperen_US
dc.identifier.spage110en_US
dc.identifier.epage114en_US
dc.identifier.doi10.1109/IPAS.2018.8708864en_US
dcterms.abstractA method designed to reconstruct outdoor 3D building models automatically from a point cloud is presented in this paper. The proposed approach starts with building detection using spectral and spatial data from the UAV point cloud to remove non-building features. RANSAC, modified convex hull, and line growing algorithms are used to extract main roof planes and their boundaries. Roof planes are adjusted to each other using geometrical constraints, the height of each plane is estimated and a 3D model for the whole structure is constructed with LoD2. The key contribution of this approach is using a hybrid approach of model-driven with statistical analysis for modeling complex structures from a noisy point cloud. The reconstructed model shows that the workflow is sufficient to describe the whole building structure in the required LoD.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France, 12-14 December 2018, p. 110-114en_US
dcterms.issued2018-
dc.identifier.scopus2-s2.0-85066330317-
dc.relation.conferenceInternational Image Processing, Applications and Systems Conference [IPAS]-
dc.description.validate202305 bckw-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberLSGI-0287-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS28990400-
dc.description.oaCategoryGreen (AAM)en_US
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