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hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierMéthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution [MATIS]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBIASUTTI, Pierre
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorAUJOL, Jean-François
hal.structure.identifierMéthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution [MATIS]
dc.contributor.authorBRÉDIF, Mathieu
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBUGEAU, Aurélie
dc.date.accessioned2024-04-04T03:02:08Z
dc.date.available2024-04-04T03:02:08Z
dc.date.issued2018-11-24
dc.identifier.issn1077-3142
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192953
dc.description.abstractEnThis paper presents a fully automatic framework for the generation of so-called LiDAR orthoimages (i.e. 2D raster maps of the reflectance and height LiDAR samples) from ground-level LiDAR scans. Beyond the Digital Surface Model (DSM or heightmap) provided by the height orthoimage, the pro- posed method cost-effectively generates a reflectance channel that is easily interpretable by human operators without relying on any optical acquisition, calibration and registration. Moreover, it com- monly achieves very high resolutions (1cm2 per pixel), thanks to the typical sampling density of static or mobile LiDAR scans.Compared to orthoimages generated from aerial datasets, the proposed LiDAR orthoimages are ac- quired from the ground level and thus do not suffer occlusions from hovering objects (trees, tunnels, bridges ...), enabling their use in a number of urban applications such as road network monitoring and management, as well as precise mapping of the public space e.g. for accessibility applications or management of underground networks.Its generation and usability however faces two issues : (i) the inhomogeneous sampling density of LiDAR point clouds and (ii) the presence of masked areas (holes) behind occluders, which include, in a urban context, cars, tree trunks, poles, pedestrians... (i) is addressed by first projecting the point cloud on a 2D-pixel grid so as to generate sparse and noisy reflectance and height images from which dense images estimated using a joint anisotropic diffusion of the height and reflectance channels. (ii) LiDAR shadow areas are detected by analysing the diffusion results so that they can be inpainted using an examplar-based method, guided by an alignment prior.Results on real mobile and static acquisition data demonstrate the effectiveness of the proposed pipeline in generating a very high resolution LiDAR orthoimage of reflectance and height while filling holes of various sizes in a visually satisfying way.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.eninpainting
dc.subject.endiffusion
dc.subject.enOrthophoto
dc.subject.enOrthoimage
dc.subject.enlidar
dc.subject.enmms
dc.subject.envariational
dc.title.enDiffusion and inpainting of reflectance and height LiDAR orthoimages
dc.typeArticle de revue
dc.identifier.doi10.1016/j.cviu.2018.10.011
dc.subject.halInformatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
bordeaux.journalComputer Vision and Image Understanding
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01322822
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01322822v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computer%20Vision%20and%20Image%20Understanding&rft.date=2018-11-24&rft.eissn=1077-3142&rft.issn=1077-3142&rft.au=BIASUTTI,%20Pierre&AUJOL,%20Jean-Fran%C3%A7ois&BR%C3%89DIF,%20Mathieu&BUGEAU,%20Aur%C3%A9lie&rft.genre=article


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