Stochastic surface mesh reconstruction

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dc.authorid0000-0002-5279-8056
dc.authorid0000-0002-1510-8677
dc.authorid0000-0001-8195-9333
dc.contributor.authorÖzendi, Mustafaen_US
dc.contributor.authorAkça, Mehmet Devrimen_US
dc.contributor.authorTopan, Hüseyinen_US
dc.date.accessioned2019-01-03T23:48:49Z
dc.date.available2019-01-03T23:48:49Z
dc.date.issued2018-05-30
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Civil Engineeringen_US
dc.descriptionThis research was funded by TUBITAK – The Scientific and Technological Research Council of Turkey (Project ID: 115Y239) and by the Scientific Research Projects of Bülent Ecevit University (Project ID: 2015-47912266-01)en_US
dc.description.abstractA generic and practical methodology is presented for 3D surface mesh reconstruction from the terrestrial laser scanner (TLS) derived point clouds. It has two main steps. The first step deals with developing an anisotropic point error model, which is capable of computing the theoretical precisions of 3D coordinates of each individual point in the point cloud. The magnitude and direction of the errors are represented in the form of error ellipsoids. The following second step is focused on the stochastic surface mesh reconstruction. It exploits the previously determined error ellipsoids by computing a point-wise quality measure, which takes into account the semi-diagonal axis length of the error ellipsoid. The points only with the least errors are used in the surface triangulation. The remaining ones are automatically discarded.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationÖzendi, M., Akça, M. D. & Topan, H. (2018). Stochastic surface mesh reconstruction. Paper presented at the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2), 805-812. doi:10.5194/isprs-archives-XLII-2-805-2018en_US
dc.identifier.doi10.5194/isprs-archives-XLII-2-805-2018
dc.identifier.endpage812
dc.identifier.issn1682-1750
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85048373249
dc.identifier.scopusqualityN/A
dc.identifier.startpage805
dc.identifier.urihttps://hdl.handle.net/11729/1440
dc.identifier.urihttp://dx.doi.org/10.5194/isprs-archives-XLII-2-805-2018
dc.identifier.volume42
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAkça, Mehmet Devrimen_US
dc.institutionauthorid0000-0002-1510-8677
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciencesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSurfacesen_US
dc.subjectSurface reconstructionen_US
dc.subjectMesh denoisingen_US
dc.subjectError ellipsoiden_US
dc.subjectPoint error modelen_US
dc.subjectSurface triangulationen_US
dc.subjectTLS point clouden_US
dc.subjectVariance-covariance propagationen_US
dc.subjectErrorsen_US
dc.subjectMesh generationen_US
dc.subjectStochastic systemsen_US
dc.subjectSurveying instrumentsen_US
dc.subjectTriangulationen_US
dc.subjectError ellipsoidsen_US
dc.subjectError modelen_US
dc.subjectPoint clouden_US
dc.titleStochastic surface mesh reconstructionen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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