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3D watertight mesh generation with uncertainties from ubiquitous data / Laurent Caraffa (2016)  

Public Titre : 3D watertight mesh generation with uncertainties from ubiquitous data Type de document : Article/Communication Auteurs : Laurent Caraffa , Auteur ; Mathieu Brédif , Auteur ; Bruno Vallet , Auteur Congrès : ACCV 2016, 13th Asian Conference on Computer Vision (20 - 24 novembre 2016; Taipei, Taiwan), Commanditaire Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2016 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 10114 Projets : IQmulus / Brédif, Mathieu Importance : pp 377 - 391 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] algorithme Graph-Cut [Termes descripteurs IGN] carte de confiance [Termes descripteurs IGN] distance de Hausdorff [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] maille triangulaire [Termes descripteurs IGN] reconstruction d'objet [Termes descripteurs IGN] seuillage [Termes descripteurs IGN] surface imperméable [Termes descripteurs IGN] théorie de Dempster-Shafer Résumé : (auteur) In this paper, we propose a generic framework for watertight mesh generation with uncertainties that provides a confidence measure on each reconstructed mesh triangle. Its input is a set of vision-based or Lidar-based 3D measurements which are converted to a set of mass functions that characterize the level of confidence on the occupancy of the scene as occupied, empty or unknown based on Dempster-Shafer Theory. The output is a multi-label segmentation of the ambient 3D space expressing the confidence for each resulting volume element to be occupied or empty. While existing methods either sacrifice watertightness (local methods) or need to introduce a smoothness prior (global methods), we derive a per-triangle confidence measure that is able to gradually characterize when the resulting surface patches are certain due to dense and coherent measurements and when these patches are more uncertain and are mainly present to ensure smoothness and/or watertightness. The surface mesh reconstruction is formulated as a global energy minimization problem efficiently optimized with the α-expansion algorithm. We claim that the resulting confidence measure is a good estimate of the local lack of sufficiently dense and coherent input measurements, which would be a valuable input for the next-best-view scheduling of a complementary acquisition. Beside the new formulation, the proposed approach achieves state-of-the-art results on surface reconstruction benchmark. It is robust to noise, manages high scale disparity and produces a watertight surface with a small Hausdorff distance in uncertainty area thanks to the multi-label formulation. By simply thresholding the result, the method shows a good reconstruction quality compared to local algorithms on high density data. This is demonstrated on a large scale reconstruction combining real-world datasets from airborne and terrestrial Lidar and on an indoor scene reconstructed from images. Numéro de notice : C2016-024 Affiliation des auteurs : IGN (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-54190-7_23 date de publication en ligne : 12/03/2017 En ligne : http://doi.org/10.1007/978-3-319-54190-7_23 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84627