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A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 61 n° 1 (January 2023) . - n° 5600812 Titre : A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction Type de document : Article/Communication Auteurs : Jiayi Li, Auteur ; Xin Huang, Auteur ; Yujin Feng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5600812 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] approche hiérarchique [Termes IGN] carte de profondeur [Termes IGN] déformation d'objet [Termes IGN] effet de profondeur cinétique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image aérienne [Termes IGN] jeu de données [Termes IGN] modèle numérique de surface [Termes IGN] modèle stéréoscopique [Termes IGN] reconstruction d'image [Termes IGN] réseau neuronal profond [Termes IGN] segmentation sémantique Résumé : (auteur) Multiview stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning-based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this article describes a new benchmark dataset called the LuoJia-MVS dataset ( https://irsip.whu.edu.cn/resources/resources_en_v2.php ), as well as a new deep neural network known as the hierarchical deformable cascade MVS network (HDC-MVSNet). The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model (DSM) built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of 3-D convolutional blocks, and “true 3-D” deformable 3-D convolutional layers are specifically designed by considering the aforementioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods and confirmed that the newly built dataset can provide an effective benchmark. Numéro de notice : A2023-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3234694 En ligne : https://doi.org/10.1109/TGRS.2023.3234694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102488 [article]