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Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds / Xiang Gao in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 143 (September 2018) . - pp 72 - 84 Titre : Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds Type de document : Article/Communication Auteurs : Xiang Gao, Auteur ; Shuhan Shen, Auteur ; Yang Zhou, Auteur ; Hainan Cui, Auteur ; Lingjie Zhu, Auteur ; Zhanyi Hu, Auteur Année de publication : 2018 Article en page(s) : pp 72 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] architecture [Termes IGN] données localisées 3D [Termes IGN] fusion de données [Termes IGN] image aérienne [Termes IGN] image terrestre [Termes IGN] modèle 3D du site [Termes IGN] patrimoine immobilier [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] temple Résumé : (Auteur) Ancient Chinese architecture 3D digitalization and documentation is a challenging task for the image based modeling community due to its architectural complexity and structural delicacy. Currently, an effective approach to ancient Chinese architecture 3D reconstruction is to merge the two point clouds, separately obtained from ground and aerial images by the SfM technique. There are two understanding issues should be specially addressed: (1) it is difficult to find the point matches between the images from different sources due to their remarkable variations in viewpoint and scale; (2) due to the inevitable drift phenomenon in any SfM reconstruction process, the resulting two point clouds are no longer strictly related by a single similarity transformation as it should be theoretically. To address these two issues, a new point cloud merging method is proposed in this work. Our method has the following characteristics: (1) the images are matched by leveraging sparse mesh based image synthesis; (2) the putative point matches are filtered by geometrical consistency check and geometrical model verification; and (3) the two point clouds are merged via bundle adjustment by linking the ground-to-aerial tracks. Extensive experiments show that our method outperforms many of the state-of-the-art approaches in terms of ground-to-aerial image matching and point cloud merging. Numéro de notice : A2018-355 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.023 Date de publication en ligne : 08/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90589 [article]

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