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Joint inpainting of depth and reflectance with visibility estimation

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierMéthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution [MATIS]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorBEVILACQUA, Marco
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorAUJOL, Jean-François
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierMéthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution [MATIS]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBIASUTTI, Pierre
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.issued2017-03
dc.identifier.issn0924-2716
dc.description.abstractEnThis paper presents a novel strategy to generate, from 3-D lidar measures, dense depth and reflectance images coherent with given color images. It also estimates for each pixel of the input images a visibility attribute. 3-D lidar measures carry multiple information, e.g. relative distances to the sensor (from which we can compute depths) and reflectances. When projecting a lidar point cloud onto a reference image plane, we generally obtain sparse images, due to undersampling. Moreover, lidar and image sensor positions typically differ during acquisition; therefore points belonging to objects that are hidden from the image view point might appear in the lidar images. The proposed algorithm estimates the complete depth and reflectance images, while concurrently excluding those hidden points. It consists in solving a joint (depth and reflectance) variational image inpainting problem, with an extra variable to concurrently estimate handling the selection of visible points. As regularizers, two coupled total variation terms are included to match, two by two, the depth, reflectance, and color image gradients. We compare our algorithm with other image-guided depth upsampling methods, and show that, when dealing with real data, it produces better inpainted images, by solving the visibility issue.
dc.language.isoen
dc.publisherElsevier
dc.subject.enInpainting
dc.subject.enTotal Variation
dc.subject.enDepth Maps
dc.subject.enLidar
dc.subject.enReflectance
dc.subject.enPoint Cloud
dc.subject.enVisibility
dc.title.enJoint inpainting of depth and reflectance with visibility estimation
dc.typeArticle de revue
dc.identifier.doi10.1016/j.isprsjprs.2017.01.005
dc.subject.halInformatique [cs]/Traitement des images
dc.subject.halInformatique [cs]/Géométrie algorithmique [cs.CG]
bordeaux.journalISPRS Journal of Photogrammetry and Remote Sensing
bordeaux.page16--32
bordeaux.volume125
bordeaux.peerReviewedoui
hal.identifierhal-01439299
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01439299v1
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