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dc.contributor.authorRashidi, Abbasen
dc.contributor.authorBrilakis, Ioannisen
dc.contributor.authorVela, Patricioen
dc.date.accessioned2015-03-20T16:50:17Z
dc.date.available2015-03-20T16:50:17Z
dc.date.issued2014-07-21en
dc.identifier.citationJournal of Computing in Civil Engineering 2015, 29(6) 04014089. DOI: 10.1061/(ASCE)CP.1943-5487.0000414en
dc.identifier.issn0887-3801
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/247133
dc.description.abstractThe global scale of Point Cloud Data (PCD) generated through monocular photo/videogrammetry is unknown, and can be calculated using at least one known dimension of the scene. Measuring one or more dimensions for this purpose induces a manual step in the 3D reconstruction process; this increases the effort and reduces the speed of reconstructing scenes, and induces substantial human error in the process due to the high level of measurement accuracy needed. Other ways of measuring such dimensions are based on acquiring additional information by either using extra sensors or specific classes of objects existing in the scene; we found that these solutions are not simple, cost effective or general enough to be considered practical for reconstructing both indoor and outdoor built infrastructure scenes. To address the issue, in this paper, we propose a novel method for automatically calculating the absolute scale of built infrastructure PCD. We use a pre-measured cube for outdoor scenes and a sheet of paper for indoor environments as the calibration patterns. Assuming that the dimensions of these objects are known, the proposed method extracts the objects’ corner points in 2D video frames using a novel algorithm. The extracted corner points are then matched between the consecutive frames. Finally, the corresponding corner points are reconstructed along with other features of the scenes to determine the real world scale. To evaluate the performance of the method, ten indoor and ten outdoor cases were selected and the absolute-scale PCD for each case was computed. Results illustrated the proposed algorithm is able to reconstruct the predefined objects with a high success rate while the generated absolute scale PCD is sufficiently accurate.
dc.languageEnglishen
dc.language.isoenen
dc.publisherAmerican Society of Civil Engineers
dc.subjectAbsolute scaleen
dc.subjectMonocular videogrammetryen
dc.subjectPoint Cloud Dataen
dc.subject3D reconstructionen
dc.titleGenerating Absolute Scale Point Cloud Data of Built Infrastructure Scenes Using a Monocular Camera Settingen
dc.typeArticle
dc.description.versionThis is the accepted manuscript. The final version is available from ASCE at http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000414en
prism.number04014089en
prism.publicationDate2014en
prism.publicationNameJournal of Computing in Civil Engineeringen
prism.volume29en
rioxxterms.versionofrecord10.1061/(ASCE)CP.1943-5487.0000414en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-07-21en
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
dc.identifier.eissn1943-5487
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/I019308/1)
pubs.funder-project-idEPSRC (EP/K000314/1)
pubs.funder-project-idEPSRC (EP/L010917/1)


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