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dc.contributor.authorKehl, Christian
dc.contributor.authorBuckley, Simon John
dc.contributor.authorGawthorpe, Robert
dc.contributor.authorViola, Ivan
dc.contributor.authorHowell, John Anthony
dc.date.accessioned2016-10-11T09:52:58Z
dc.date.available2016-10-11T09:52:58Z
dc.date.issued2016
dc.PublishedISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, III(2):121-128eng
dc.identifier.issn2194-9050en_US
dc.identifier.urihttps://hdl.handle.net/1956/12963
dc.description.abstractAdding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm’s accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.en_US
dc.language.isoengeng
dc.publisherCopernicusen_US
dc.relation.ispartof<a href="http://hdl.handle.net/1956/16790" target="_blank">Visual Techniques for Geological Fieldwork Using Mobile Devices</a>en_US
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/eng
dc.subjectImage-to-Geometryeng
dc.subjectAutomatic Pose Estimationeng
dc.subjectMobile Deviceseng
dc.subjectRegistration Interfaceseng
dc.subjectVirtual Outcrop Geologyeng
dc.titleDirect image-to-geometry registration using mobile sensor dataen_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2016-07-21T07:46:29Z
dc.description.versionpublishedVersionen_US
dc.identifier.doihttps://doi.org/10.5194/isprs-annals-iii-2-121-2016
dc.identifier.cristin1368846
dc.relation.projectNorges forskningsråd: 234111


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