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Traffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 177 (March 2022) . - pp 87 - 110 Titre : Traffic sign three-dimensional reconstruction based on point clouds and panoramic images Type de document : Article/Communication Auteurs : Minye Wang, Auteur ; Rufei Liu, Auteur ; Jiben Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 87 - 110 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] correction d'image [Termes IGN] extraction de traits caractéristiques [Termes IGN] image panoramique [Termes IGN] lidar mobile [Termes IGN] reconstruction 3D [Termes IGN] semis de points [Termes IGN] signalisation routière Résumé : (auteur) Traffic signs are a very important source of information for drivers and pilotless automobiles. With the advance of Mobile LiDAR System (MLS), massive point clouds have been applied in three-dimensional digital city modelling. However, traffic signs in MLS point clouds are low density, colourless and incomplete. This paper presents a new method for the reconstruction of vertical rectangle traffic sign point clouds based on panoramic images. In this method, traffic sign point clouds are extracted based on arc feature and spatial semantic features analysis. Traffic signs in images are detected by colour and shape features and a convolutional neural network. Traffic sign point cloud and images are registered based on outline features. Finally, traffic sign points match traffic sign pixels to reconstruct the traffic sign point cloud. Experimental results have demonstrated that this proposed method can effectively obtain colourful and complete traffic sign point clouds with high resolution. Numéro de notice : A2022-254 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12398 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.1111/phor.12398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100217 [article]

Hierarchical classification of pole‐like objects in mobile laser scanning point clouds / Rufei Liu in Photogrammetric record, vol 35 n° 169 (March 2020)  

Public [article]  inPhotogrammetric record > vol 35 n° 169 (March 2020) . - pp 81 - 107 Titre : Hierarchical classification of pole‐like objects in mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Rufei Liu, Auteur ; Peng Wang, Auteur ; Zhaojin Yan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 81 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de la valeur [Termes IGN] classification ascendante hiérarchique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] lidar mobile [Termes IGN] milieu urbain [Termes IGN] semis de points [Termes IGN] valeur propre Résumé : (Auteur) For the classification of pole‐like objects (trees, lamp posts, traffic lights and traffic signs) in mobile laser scanning (MLS) point clouds, a hierarchical classification method is proposed. The method consists of three major steps. (1) The objects’ cylindrical column sections are detected based on the characteristics of arc‐like points using RANSAC after denoising. (2) These detected objects are roughly classified into trees and man‐made poles based on the azimuthal coverage of point clouds above the cylindrical column. (3) Eigenvalue analysis and the principal direction of the upper pole projections are used to differentiate lamp posts, traffic lights and traffic signs. Experimental analysis shows that the method can effectively identify different types of pole‐like objects. Numéro de notice : A2020-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12307 Date de publication en ligne : 10/01/2020 En ligne : https://doi.org/10.1111/phor.12307 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94819 [article]