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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.contributor.advisorShi, Wen-zhong (LSGI)en_US
dc.creatorAhmed, Wael Mohamed Sayed-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/10726-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleAutomatic reconstruction and modelling of 3D geometrical surfaces from unstructured point clouden_US
dcterms.abstractRecently, point clouds have been conducted to reconstruct 3D models for several applications. Three different types of modelling are conducted. Firstly, a semantic indoor geometric modelling approach (SIGMA) is designed for reconstructing parametric surface-based building models with additional models of wall-surface objects. Our approach uses a five-step process including pre-processing, 3D segmentation, layout reconstruction, wall-surface object modelling, and ceiling reconstruction. Compared to existing approaches, our approach can model complex layout structures of arbitrary ceilings with enriched wall-surface models from point cloud datasets. Quantitative evaluations demonstrate the capabilities of SIGMA on a complex real-world point cloud dataset. Secondly, detection reconstruction of outdoor structure from an image-based point cloud is proposed. The benefits of the spatial and coloured point cloud are used to isolate the structure, and primitive surfaces are detected to reconstruct the model from roof patches. The reconstructed model shows that the workflow is sufficient to describe the whole building structure in the required LOD. Finally, a proposed morphologically iterative TIN (MIT) ground filter which only requires maximum building size in processing LiDAR data. This approach applies morphological and TIN densification in an iterative way for separating ground points from off-ground ones. Experimental results using ISPRS benchmark datasets and Hong Kong LiDAR datasets reveal that MIT is effective in detecting more ground points and robust in various terrain situations.en_US
dcterms.extentxix, 144 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2020en_US
dcterms.educationalLevelPh.D.en_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHRemote sensingen_US
dcterms.LCSHImage reconstructionen_US
dcterms.LCSHImage processing -- Digital techniquesen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/10726