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Auteur Xianghong Hua |
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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]An average error-ellipsoid model for evaluating TLS point-cloud accuracy / Xijiang Chen in Photogrammetric record, vol 31 n° 153 (March - May 2016)
[article]
Titre : An average error-ellipsoid model for evaluating TLS point-cloud accuracy Type de document : Article/Communication Auteurs : Xijiang Chen, Auteur ; Guang Zhang, Auteur ; Xianghong Hua, Auteur ; Wei Xuan, Auteur Année de publication : 2016 Article en page(s) : pp 71 - 87 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur moyenne
[Termes IGN] estimation statistique
[Termes IGN] précision des données
[Termes IGN] qualité des données
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) Estimation of the accuracy of terrestrial laser scanning has become a topical research issue. Point-cloud accuracy should be clearly differentiated from the accuracy of individual points, as it is influenced by positional errors and adjacent error spaces. Furthermore, it cannot be determined by the sum of positional errors. In this paper, it is proposed that the average error-ellipsoid model is favourable for evaluating the accuracy of point clouds. Central to this model is the computation of the overlap between adjacent error ellipsoids and the determination of a functional relationship between the average volume of error ellipsoids and the point-cloud accuracy. This paper outlines the key advantages of the proposed evaluation model, such as the capability of providing an estimate of local point-cloud accuracy. The effectiveness of the model is discussed using a validation experiment. Numéro de notice : A2016-162 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12136 Date de publication en ligne : 02/03/2016 En ligne : https://doi.org/10.1111/phor.12136 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80462
in Photogrammetric record > vol 31 n° 153 (March - May 2016) . - pp 71 - 87[article]Réservation
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