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Auteur Tatsuya Ikeda |
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Automatic registration of MLS point clouds and SfM meshes of urban area / Reiji Yoshimura in Geo-spatial Information Science, vol 19 n° 3 (October 2016)
[article]
Titre : Automatic registration of MLS point clouds and SfM meshes of urban area Type de document : Article/Communication Auteurs : Reiji Yoshimura, Auteur ; Hiroaki Date, Auteur ; Satoshi Kanai, Auteur ; Ryohei Honma, Auteur ; Kazuo Oda, Auteur ; Tatsuya Ikeda, Auteur Année de publication : 2016 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme ICP
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] maille triangulaire
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] similitude
[Termes IGN] structure-from-motion
[Termes IGN] zone urbaineRésumé : (auteur) Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments efficiently. Currently, there are various methods for acquiring large-scale 3D scan data, such as Mobile Laser Scanning (MLS), Airborne Laser Scanning, Terrestrial Laser Scanning, photogrammetry and Structure from Motion (SfM). Especially, MLS is useful to acquire dense point clouds of road and road-side objects, and SfM is a powerful technique to reconstruct meshes with textures from a set of digital images. In this research, a registration method of point clouds from vehicle-based MLS (MLS point cloud), and textured meshes from the SfM of aerial photographs (SfM mesh), is proposed for creating high-quality surface models of urban areas by combining them. In general, SfM mesh has non-scale information; therefore, scale, position, and orientation of the SfM mesh are adjusted in the registration process. In our method, first, 2D feature points are extracted from both SfM mesh and MLS point cloud. This process consists of ground- and building-plane extraction by region growing, random sample consensus and least square method, vertical edge extraction by detecting intersections between the planes, and feature point extraction by intersection tests between the ground plane and the edges. Then, the corresponding feature points between the MLS point cloud and the SfM mesh are searched efficiently, using similarity invariant features and hashing. Next, the coordinate transformation is applied to the SfM mesh so that the ground planes and corresponding feature points are adjusted. Finally, scaling Iterative Closest Point algorithm is applied for accurate registration. Experimental results for three data-sets show that our method is effective for the registration of SfM mesh and MLS point cloud of urban areas including buildings. Numéro de notice : A2016--116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2016.1212517 En ligne : http://dx.doi.org/10.1080/10095020.2016.1212517 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84774
in Geo-spatial Information Science > vol 19 n° 3 (October 2016) . - pp[article]