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Semantic segmentation of urban textured meshes through point sampling / Grégoire Grzeczkowicz in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 177 - 184 Titre : Semantic segmentation of urban textured meshes through point sampling Type de document : Article/Communication Auteurs : Grégoire Grzeczkowicz, Auteur ; Bruno Vallet , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 177 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] échantillonnage de données [Termes IGN] maillage [Termes IGN] maille carrée [Termes IGN] maille texturée [Termes IGN] maille triangulaire [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] traitement de semis de points Résumé : (auteur) Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric information. To address this problem, we adopt an approach consisting in sampling a point cloud from the textured mesh, then using a point cloud semantic segmentation algorithm on this cloud, and finally using the obtained semantic to segment the initial mesh. In this paper, we study the influence of different parameters such as the sampling method, the density of the extracted cloud, the features selected (color, normal, elevation) as well as the number of points used at each training period. Our result outperforms the state-of-the-art on the SUM dataset, earning about 4 points in OA and 18 points in mIoU. Numéro de notice : A2022-427 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2022-177-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-177-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100733 [article]

3D mapping at IGN / Bruno Vallet (2022)

Public Titre : 3D mapping at IGN Type de document : Article/Communication Auteurs : Bruno Vallet , Auteur Editeur : Stuttgart : Institüt für Photogrammetrie der Universität Stuttgart Année de publication : 2022 Conférence : PhoWo 2022, 58th Photogrammetric Week 13/09/2022 16/09/2022 Stuttgart Allemagne Langues : Anglais (eng) Numéro de notice : C2022-035 Affiliation des auteurs : UGE-LASTIG (2020- ) Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101579

A 3D segments based algorithm for heterogeneous data registration / Rahima Djahel (2022)  

Public Titre : A 3D segments based algorithm for heterogeneous data registration Type de document : Article/Communication Auteurs : Rahima Djahel, Auteur ; Pascal Monasse, Auteur ; Bruno Vallet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B1 Projets : 1-Pas de projet / Conférence : ISPRS 2022, XXIV ISPRS international congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 129 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme du recuit simulé [Termes IGN] données hétérogènes [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] orthoimage [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] segment de droite [Termes IGN] superposition de données Résumé : (auteur) Combining image and LiDAR draws increasing interest in surface reconstruction, city and building modeling for constructing 3D virtual reality models because of their complementary nature. However, to gain from this complementarity, these data sources must be precisely registered. In this paper, we propose a new primitive based registration algorithm that takes 3D segments as features. The objective of the proposed algorithm is to register heterogeneous data. The heterogeneity is both in data type (image and LiDAR) and acquisition platform (terrestrial and aerial). Our algorithm starts by extracting 3D segments from LiDAR and image data with state of the art algorithms. Then it clusters the 3D segments of each data according to their directions. The obtained clusters are associated to find possible rotations, then 3D segments from associated clusters are matched in order to find the translation and scale factor minimizing a distance criteria between the two sets of 3D segments. Two optimizers (simulated annealing and RANSAC) are tested to minimize this distance criterion, first on synthetic data, then on real data. The experiments carried out demonstrate the robustness and speed of RANSAC compared to simulated annealing. Numéro de notice : C2022-018 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B1-2022-129-2022 Date de publication en ligne : 30/05/2022 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLIII-B1-2022-129-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100844

Deep surface reconstruction from point clouds with visibility information / Raphaël Sulzer (2022)  

Public Titre : Deep surface reconstruction from point clouds with visibility information Type de document : Article/Communication Auteurs : Raphaël Sulzer , Auteur ; Loïc Landrieu , Auteur ; Alexandre Boulch, Auteur ; Renaud Marlet, Auteur ; Bruno Vallet , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Projets : BIOM / Vallet, Bruno Importance : 13 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] reconstruction d'objet [Termes IGN] semis de points [Termes IGN] visibilité Résumé : (auteur) Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful information regarding space occupancy and surface orientation. In this paper, we present two simple ways to augment raw point clouds with visibility information, so it can directly be leveraged by surface reconstruction networks with minimal adaptation. Our proposed modifications consistently improve the accuracy of generated surfaces as well as the generalization ability of the networks to unseen shape domains. Numéro de notice : P2022-002 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2202.01810 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.48550/arXiv.2202.01810 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99811

Detecting openings for indoor/outdoor registration / Rahima Djahel (2022)  

Public Titre : Detecting openings for indoor/outdoor registration Type de document : Article/Communication Auteurs : Rahima Djahel, Auteur ; Bruno Vallet , Auteur ; Pascal Monasse, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B1 Projets : 1-Pas de projet / Vallet, Bruno Conférence : ISPRS 2022, XXIV ISPRS international congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 177 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] lancer de rayons [Termes IGN] ouverture (bâtiment) [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D du bâti [Termes IGN] segment de droite [Termes IGN] semis de points [Termes IGN] superposition de données Résumé : (auteur) Indoor/Outdoor modeling of buildings is an important issue in the field of building life cycle management. It is seen as a joint process where the two aspects collaborate to take advantage of their semantic and geometric complementary. This global approach will allow a more complete, correct, precise and coherent reconstruction of the buildings. The first issue of such modeling is thus to precisely register this data. The lack of overlap between indoor and outdoor data is the most encountered obstacle, more so when both data sets are acquired separately and using different types of sensors. As an opening in the façade is the unique common entity that can be seen from inside and outside, it can help the registration of indoor and outdoor point clouds. So it must be automatically, accurately and efficiently extracted. In this paper, we start by proposing a very efficient algorithm to detect openings with great precision in both indoor and outdoor scans. Afterwards, we integrate them in a registration framework. As an opening is defined by a rectangular shape composed of four segments, two horizontal and two vertical, we can write our registration problem as a minimization of a global robust distance between two segment sets and propose a robust approach to minimize this distance using the RANSAC paradigm. Numéro de notice : C2022-023 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B1-2022-177-2022 Date de publication en ligne : 30/05/2022 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLIII-B1-2022-177-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100849

Learning multi-view aggregation in the wild for large-scale 3D semantic segmentation / Damien Robert (2022)  

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Registration of heterogenous data for urban modeling / Rahima Djahel (2022)  

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Scalable surface reconstruction with Delaunay-Graph neural networks / Raphaël Sulzer in Computer graphics forum, vol 40 n° 5 (2021)  

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Towards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)  

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AI4GEO: a data intelligence platform for 3D geospatial mapping / Pierre-Marie Brunet (2021)  

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Efficiently distributed watertight surface reconstruction / Laurent Caraffa (2021)  

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Evaluating surface mesh reconstruction of open scenes / Yanis Marchand (2021)  

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

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Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Cartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)  

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