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Preface: the 2021 edition of the XXIVth ISPRS congress / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2021 (July 2021)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2021 (July 2021) . - pp 1 - 5 Titre : Preface: the 2021 edition of the XXIVth ISPRS congress Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; Florent Lafarge, Auteur ; Martyna Poreba , Auteur ; Teng Wu , Auteur ; Gaétan Bahl, Auteur ; Min Yu, Auteur ; Anatol Garioud , Auteur ; Yizi Chen , Auteur ; San Jiang, Auteur ; Michael Ying Yang, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 1, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 1 Article en page(s) : pp 1 - 5 Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) We report key elements and figures related to the proceedings of the 2021 edition of the XXIVth ISPRS Congress. Similarly to 2020, the COVID-19 pandemic caused global travel challenges and restrictions for the first half of 2021. Consequently, the physical Congress re-scheduled from June 2020 to July 2021 was again postponed to June 2022, still in Nice (France). Papers were already submitted and the ISPRS Council decided to carry out the review process and the publication of the proceedings of the papers submitted under the label “2021 Edition”. The authors of published papers had the opportunity to present their work during a Digital Event, this year scheduled the same week as the planned Congress (5–9 July 2021). Numéro de notice : A2021-613 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2021-1-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-4-2021-1-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97948 [article]

A new stereo dense matching benchmark dataset for deep learning / Teng Wu (2021)  

Public Titre : A new stereo dense matching benchmark dataset for deep learning Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Marc Pierrot-Deseilligny , Auteur ; Ewelina Rupnik , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Projets : AI4GEO / Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 405 - 412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] appariement de données localisées [Termes IGN] appariement dense [Termes IGN] apprentissage profond [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] jeu de données localisées [Termes IGN] parangonnage [Termes IGN] photogrammétrie aérienne [Termes IGN] reconstruction 3D Résumé : (auteur) Stereo dense matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, for example Middlebury and KITTI stereo. However, it is not easy to find a training dataset for aerial photogrammetry. Generating ground truth data for real scenes is a challenging task. In the photogrammetry community, many evaluation methods use digital surface models (DSM) to generate the ground truth disparity for the stereo pairs, but in this case interpolation may bring errors in the estimated disparity. In this paper, we publish a stereo dense matching dataset based on ISPRS Vaihingen dataset, and use it to evaluate some traditional and deep learning based methods. The evaluation shows that learning-based methods outperform traditional methods significantly when the fine tuning is done on a similar landscape. The benchmark also investigates the impact of the base to height ratio on the performance of the evaluated methods. The dataset can be found in https://github.com/whuwuteng/benchmark_ISPRS2021. Numéro de notice : C2021-012 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2021-405-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-405-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98066

Moving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans / Teng Wu (2020)  

Public contenu dans XXIV ISPRS Congress, Commission 2 / Nicolas Paparoditis (2020)   Titre : Moving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Cédric Demonceaux, Auteur ; Jingbin Liu, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 2 Importance : pp 507 - 514 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 2D [Termes IGN] espace intérieur [Termes IGN] fusion de données [Termes IGN] objet mobile [Termes IGN] reconstruction 3D [Termes IGN] semis de points Résumé : (auteur) Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system). Numéro de notice : C2020-008 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2020-507-2020 Date de publication en ligne : 12/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-507-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95659