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dc.contributorFaculty of Construction and Environmenten_US
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.contributor.advisorShi, Wenzhong John (LSGI)-
dc.creatorWang, Ruolei-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/10537-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleAutomatic extraction of indoor objects by using lidar point clouden_US
dcterms.abstractRecently, with the bring forward of concept of 'Smart City', the BIM technology is commonly accepted by users and continues to develop rapidly. Because of its rapid data collection, high accuracy and being less limited, laser scanning technology is increasingly used in data acquisition of building 3D modeling. Simultaneously, in order to improve the service ability of management and analysis of BIM, automatic extraction of windows and doors is a necessary work which can improve work efficiency and increase the level of detail of BIM. However, high accuracy of indoor scene description means a great quantity of data and high requirement of processing computer hardware. After introduction and analysis of previous related researches, principles and characteristics of laser scanning, and some classic point cloud processing algorithms. By using indoor LIDAR point clouds, an automatic extraction of window and doors method based on both position relationship and intensity correlation has been proposed in this dissertation. The core idea of the algorithm can be concluded as, after wall extraction, re-projecting the clutter within specific range back to the position where they should be, thereby obtaining the door and window coordinates. By using a program written in C++ and some related function libraries, the proposed algorithm is tested with four groups of indoor point clouds. After analysis and evaluation of experimental results, the proposed method is discussed and concluded, in addition, future works are listed aimed at the challenges met during the test.en_US
dcterms.extentvii, 93 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2017en_US
dcterms.educationalLevelM.Sc.en_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHBuilding information modelingen_US
dcterms.LCSHBuildingsā€”Computer-aided designen_US
dcterms.LCSHRemote sensingen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/10537