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A new GPU bundle adjustment method for large-scale data / Zhou Shunping ; Xiong Xiaodong ; Junfeng Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 9 (September 2017) . - pp 633 - 641 Titre : A new GPU bundle adjustment method for large-scale data Type de document : Article/Communication Auteurs : Zhou Shunping, Auteur ; Xiong Xiaodong, Auteur ; Junfeng Zhu, Auteur Année de publication : 2017 Article en page(s) : pp 633 - 641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme du gradient conjugué [Termes IGN] analyse comparative [Termes IGN] compensation par faisceaux [Termes IGN] jeu de données [Termes IGN] processeur graphique [Termes IGN] traitement parallèle Résumé : (Auteur) We developed a fast and effective bundle adjustment method for large-scale datasets. The preconditioned conjugate gradient (PCG) algorithm and GPU parallel computing technology are simultaneously applied to deal with large-scale data and to accelerate the bundle adjustment process. The whole bundle adjustment process is modified to enable parallel computing. The critical optimization on parallel task assignment and GPU memory usage are specified. The proposed method was tested using 10 datasets. The traditional Levenberg Marquardt (LM) method, advanced PCG method, Wu's method and the proposed GPU parallel computing method are all compared and analyzed. Preliminary results have shown that the proposed method can process a large-scale dataset with about 13,000 images in less than three minutes on a common computer with GPU device. The efficiency of the proposed method is about the same with Wu's method while the accuracy is better. Numéro de notice : A2017-609 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.9.633 En ligne : https://doi.org/10.14358/PERS.83.9.633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86887 [article]

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