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Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data / André Dittrich in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 195 – 208 Titre : Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data Type de document : Article/Communication Auteurs : André Dittrich, Auteur ; Martin Weinmann, Auteur ; Stefan Hinz, Auteur Année de publication : 2017 Article en page(s) : pp 195 – 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] bruit (théorie du signal) [Termes IGN] calcul tensoriel [Termes IGN] discrétisation [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] lasergrammétrie [Termes IGN] méthode robuste [Termes IGN] restitution lasergrammétrique [Termes IGN] semis de points [Termes IGN] valeur propre Résumé : (auteur) In photogrammetry, remote sensing, computer vision and robotics, a topic of major interest is represented by the automatic analysis of 3D point cloud data. This task often relies on the use of geometric features amongst which particularly the ones derived from the eigenvalues of the 3D structure tensor (e.g. the three dimensionality features of linearity, planarity and sphericity) have proven to be descriptive and are therefore commonly involved for classification tasks. Although these geometric features are meanwhile considered as standard, very little attention has been paid to their accuracy and robustness. In this paper, we hence focus on the influence of discretization and noise on the most commonly used geometric features. More specifically, we investigate the accuracy and robustness of the eigenvalues of the 3D structure tensor and also of the features derived from these eigenvalues. Thereby, we provide both analytical and numerical considerations which clearly reveal that certain features are more susceptible to discretization and noise whereas others are more robust. Numéro de notice : A2017-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.012 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84512 [article]

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