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SDF-2-SDF registration for real-time 3D reconstruction from RGB-D data / Miroslava Slavcheva in International journal of computer vision, vol 126 n° 6 (June 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 6 (June 2018) . - pp 615 - 636 Titre : SDF-2-SDF registration for real-time 3D reconstruction from RGB-D data Type de document : Article/Communication Auteurs : Miroslava Slavcheva, Auteur ; Wadim Kehl, Auteur ; Nassir Navab, Auteur ; Slobodan Ilic, Auteur Année de publication : 2018 Article en page(s) : pp 615 - 636 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] contrainte géométrique [Termes IGN] estimation de pose [Termes IGN] image RVB [Termes IGN] Kinect [Termes IGN] méthode de réduction d'énergie [Termes IGN] optimisation (mathématiques) [Termes IGN] reconstruction d'objet [Termes IGN] semis de points [Termes IGN] voxel Mots-clés libres : simultaneous localization and mapping (SLAM) Résumé : (Auteur) We tackle the task of dense 3D reconstruction from RGB-D data. Contrary to the majority of existing methods, we focus not only on trajectory estimation accuracy, but also on reconstruction precision. The key technique is SDF-2-SDF registration, which is a correspondence-free, symmetric, dense energy minimization method, performed via the direct voxel-wise difference between a pair of signed distance fields. It has a wider convergence basin than traditional point cloud registration and cloud-to-volume alignment techniques. Furthermore, its formulation allows for straightforward incorporation of photometric and additional geometric constraints. We employ SDF-2-SDF registration in two applications. First, we perform small-to-medium scale object reconstruction entirely on the CPU. To this end, the camera is tracked frame-to-frame in real time. Then, the initial pose estimates are refined globally in a lightweight optimization framework, which does not involve a pose graph. We combine these procedures into our second, fully real-time application for larger-scale object reconstruction and SLAM. It is implemented as a hybrid system, whereby tracking is done on the GPU, while refinement runs concurrently over batches on the CPU. To bound memory and runtime footprints, registration is done over a fixed number of limited-extent volumes, anchored at geometry-rich locations. Extensive qualitative and quantitative evaluation of both trajectory accuracy and model fidelity on several public RGB-D datasets, acquired with various quality sensors, demonstrates higher precision than related techniques. Numéro de notice : A2018-410 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1057-z Date de publication en ligne : 18/12/2017 En ligne : https://doi.org/10.1007/s11263-017-1057-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90884 [article]