Détail de l'auteur
Auteur Bufan Zhao |
Documents disponibles écrits par cet auteur (1)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externes
Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting / Bufan Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting Type de document : Article/Communication Auteurs : Bufan Zhao, Auteur ; Xianghong Hua, Auteur ; Kegen Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7890 - 7907 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données lidar
[Termes IGN] itération
[Termes IGN] modélisation 3D
[Termes IGN] processus gaussien
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] regroupement de points
[Termes IGN] scène intérieure
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Indoor scene segmentation based on 3-D laser point cloud is important for rebuilding and classification, especially for permanent building structure. However, the existing segmentation methods mainly focus on the large-scale planar structures but ignore the other sharp structures and details, which would cause accuracy degradation in scene reconstruction. To handle this issue, an iterative Gaussian mapping-based segmentation strategy has been proposed in this article, which goes from rough segmentation to refined one iteratively to decompose the indoor scene into detectable point cloud clusters layer by layer. An improved model fitting algorithm based on the maximum likelihood estimation sampling consensus (MLESAC) algorithm is proposed for refined segmentation, which is called the Prior-MLESAC algorithm, to deal with the extraction of both vertical and nonvertical planar and cylindrical structures. The experimental results demonstrate that planar and cylindrical structures are segmented more completely by the proposed strategy, and more details of the indoor structure are restored than other existing methods. Numéro de notice : A2020-681 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2984943 Date de publication en ligne : 16/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2984943 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96205
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7890 - 7907[article]