A point cloud filtering method based on anisotropic error model

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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, Devrimen_US
dc.contributor.authorTopan, Hüseyinen_US
dc.date.accessioned2023-09-13T09:45:28Z
dc.date.available2023-09-13T09:45:28Z
dc.date.issued2023-12
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Civil Engineeringen_US
dc.descriptionThis study was supported by TUBITAK – The Scientific and Technological Research Council of Türkiye (project ID: 115Y239); and the Scientific Research Projects of Zonguldak Bülent Ecevit University (project ID: 2015‐47912266‐01).en_US
dc.description.abstractMany modelling applications require 3D meshes that should be generated from filtered/cleaned point clouds. This paper proposes a methodology for filtering of terrestrial laser scanner (TLS)-derived point clouds, consisting of two main parts: an anisotropic point error model and the subsequent decimation steps for elimination of low-quality points. The point error model can compute the positional quality of any point in the form of error ellipsoids. It is formulated as a function of the angular/mechanical stability, sensor-to-object distance, laser beam's incidence angle and surface reflectivity, which are the most dominant error sources. In a block of several co-registered point clouds, some parts of the target object are sampled by multiple scans with different positional quality patterns. This situation results in redundant data. The proposed decimation steps removes this redundancy by selecting only the points with the highest positional quality. Finally, the Good, Bad, and the Better algorithm, based on the ray-tracing concept, was developed to remove the remaining redundancy due to the Moiré effects. The resulting point cloud consists of only the points with the highest positional quality while reducing the number of points by factor 10. This novel approach resulted in final surface meshes that are accurate, contain predefined level of random errors and require almost no manual intervention.en_US
dc.description.sponsorshipScientific Research Projects of Zonguldak Bülent Ecevit Universityen_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumuen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationÖzendi, M., Akça, D. & Topan, H. (2023). A point cloud filtering method based on anisotropic error model. Photogrammetric Record, 38(184), 460-497. doi:10.1111/phor.12460en_US
dc.identifier.doi10.1111/phor.12460
dc.identifier.endpage497
dc.identifier.issn0031-868X
dc.identifier.issn1477-9730
dc.identifier.issue184
dc.identifier.scopus2-s2.0-85169608358
dc.identifier.scopusqualityQ2
dc.identifier.startpage460
dc.identifier.urihttps://hdl.handle.net/11729/5707
dc.identifier.urihttp://dx.doi.org/10.1111/phor.12460
dc.identifier.volume38
dc.identifier.wosWOS:001059860100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorAkça, Devrimen_US
dc.institutionauthorid0000-0002-1510-8677
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherJohn Wiley and Sons Incen_US
dc.relation.ispartofPhotogrammetric Recorden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectError ellipsoiden_US
dc.subjectPoint cloudsen_US
dc.subjectPoint error modelen_US
dc.subjectQuality evaluationen_US
dc.subjectSurface reconstructionen_US
dc.subjectTerrestrial laser scanningen_US
dc.subjectAnisotropyen_US
dc.subjectLaser applicationsen_US
dc.subjectLaser beamsen_US
dc.subjectRandom errorsen_US
dc.subjectSeebeck effecten_US
dc.subjectSurveying instrumentsen_US
dc.subjectThree dimensional computer graphicsen_US
dc.subjectAnisotropic errorsen_US
dc.subjectError ellipsoidsen_US
dc.subjectError modelingen_US
dc.subjectFiltering methoden_US
dc.subjectPoint error modelen_US
dc.subjectPoint-cloudsen_US
dc.subjectQuality evaluationen_US
dc.subjectRemote-sensingen_US
dc.subjectRedundancyen_US
dc.subjectTo-plane registrationen_US
dc.subjectConsolidationen_US
dc.subjectCalibrationen_US
dc.subjectUncertaintyen_US
dc.titleA point cloud filtering method based on anisotropic error modelen_US
dc.typeArticleen_US

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