Centre dedocumentation
scientifique

AI4GEO: LOD0 Generation for 3D building models / Pierre Lassalle (2023)  

Public Titre : AI4GEO: LOD0 Generation for 3D building models Type de document : Article/Communication Auteurs : Pierre Lassalle, Auteur ; Bruno Vallet , Auteur ; Etienne Le Bihan, Auteur ; Pierre-Marie Brunet, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2023 Projets : AI4GEO / Conférence : JURSE 2023, Joint Urban Remote Sensing Event 17/05/2023 19/05/2023 Heraklion Grèce Proceedings IEEE Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] image satellite [Termes IGN] niveau de détail [Termes IGN] reconstruction 3D du bâti [Termes IGN] zone urbaine Résumé : (Auteur) Recent studies on Earth observation are improved by the proliferation of imaging sensors able to capture large datasets with a high spatial resolution. As a result, many approaches have been proposed for 3D modeling, remote sensing (RS), image processing and mapping. In this scope, three-dimensional (3D) mapping of urban areas has a great potential to provide the user with a precise scene understanding. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information with new added-value services. This paper will first introduce the AI4GEO initiative, context and overall objectives. It will then present the current status regarding 3D reconstruction of urban areas, in particular LOD0 building shape generation using satellite data. Numéro de notice : C2023-010 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE57346.2023.10144155 Date de publication en ligne : 08/06/2023 En ligne : https://doi.org/10.1109/JURSE57346.2023.10144155 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103311

Mobile mapping mesh change detection and update / Teng Wu (2023)  

Public Titre : Mobile mapping mesh change detection and update Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Cédric Demonceaux, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : 7 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] maillage par triangles [Termes IGN] mosaïquage d'images [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] Stéréopolis [Termes IGN] système de numérisation mobile [Termes IGN] vision par ordinateur Résumé : (auteur) Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simu- lation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions. In this paper, we propose a fully automatic pipeline to address these two issues by formulating the problem of merging meshes with different quality, coverage and acquisition time. Our method is based on a combined distance and visibility based change detection, a time series analysis to assess the sustainability of changes, a mesh mosaicking based on a global boolean optimization and finally a stitching of the resulting mesh pieces boundaries with triangle strips. Finally, our method is demonstrated on Robotcar and Stereopolis datasets. Numéro de notice : P2023-003 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2303.07182 Date de publication en ligne : 13/03/2023 En ligne : https://doi.org/10.48550/arXiv.2303.07182 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102860

PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)  

Public Titre : PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Marc Pierrot-Deseilligny , Auteur Editeur : Computer vision foundation CVF Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition workshops 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : pp 6526 - 6535 Note générale : bibliographie voir aussi https://openaccess.thecvf.com/content/CVPR2023W/PCV/supplemental/Wu_PSMNet-FusionX3_LiDAR-Guided_Deep_CVPRW_2023_supplemental.pdf Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] appariement dense [Termes IGN] apprentissage profond [Termes IGN] chaîne de traitement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image aérienne à axe vertical [Termes IGN] scène 3D [Termes IGN] Triangulated Irregular Network Résumé : (auteur) Dense image matching (DIM) and LiDAR are two complementary techniques for recovering the 3D geometry of real scenes. While DIM provides dense surfaces, they are often noisy and contaminated with outliers. Conversely, LiDAR is more accurate and robust, but less dense and more expensive compared to DIM. In this work, we investigate learning-based methods to refine surfaces produced by photogrammetry with sparse LiDAR point clouds. Unlike the current state-of-the-art approaches in the computer vision community, our focus is on aerial acquisitions typical in photogrammetry. We propose a densification pipeline that adopts a PSMNet backbone with triangulated irregular network interpolation based expansion, feature enhancement in cost volume, and conditional cost volume normalization, i.e. PSMNet-FusionX3. Our method works better on low density and is less sensitive to distribution, demonstrating its effectiveness across a range of LiDAR point cloud densities and distributions, including analyses of dataset shifts. Furthermore, we have made both our aerial (image and disparity) dataset and code available for public use. Further information can be found at https://github.com/ whuwuteng/PSMNet-FusionX3. Numéro de notice : C2023-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication DOI : sans En ligne : https://openaccess.thecvf.com/content/CVPR2023W/PCV/papers/Wu_PSMNet-FusionX3_Li [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103277

A survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)  

Public Titre : A survey and benchmark of automatic surface reconstruction from point clouds Type de document : Article/Communication Auteurs : Raphaël Sulzer , Auteur ; Loïc Landrieu , Auteur ; Renaud Marlet, Auteur ; Bruno Vallet , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Projets : BIOM / Vallet, Bruno Importance : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] benchmark spatial [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] reconstruction d'objet [Termes IGN] semis de points Résumé : (auteur) We survey and benchmark traditional and novel learning-based algorithms that address the problem of surface reconstruction from point clouds. Surface reconstruction from point clouds is particularly challenging when applied to real-world acquisitions, due to noise, outliers, non-uniform sampling and missing data. Traditionally, different handcrafted priors of the input points or the output surface have been proposed to make the problem more tractable. However, hyperparameter tuning for adjusting priors to different acquisition defects can be a tedious task. To this end, the deep learning community has recently addressed the surface reconstruction problem. In contrast to traditional approaches, deep surface reconstruction methods can learn priors directly from a training set of point clouds and corresponding true surfaces. In our survey, we detail how different handcrafted and learned priors affect the robustness of methods to defect-laden input and their capability to generate geometric and topologically accurate reconstructions. In our benchmark, we evaluate the reconstructions of several traditional and learning-based methods on the same grounds. We show that learning-based methods can generalize to unseen shape categories, but their training and test sets must share the same point cloud characteristics. We also provide the code and data to compete in our benchmark and to further stimulate the development of learning-based surface reconstruction: https://github.com/raphaelsulzer/dsr-benchmark. Numéro de notice : P2023-004 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2301.13656 Date de publication en ligne : 31/01/2023 En ligne : https://hal.science/hal-03968453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102847

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 / Vallet, Bruno 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 triangulaire [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] traitement de semis de points Mots-clés libres : maille texturée (maille qui porte l'information géométrique et radiométrique) 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)

Permalink

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

Permalink

AI4GEO: A path from 3D model to digital twin / Pierre-Marie Brunet (2022)  

Permalink

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

Permalink

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

Permalink

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

Permalink

Learning surface reconstruction from point clouds in the wild / Raphaël Sulzer (2022)  

Permalink

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

Permalink

Scaling up and evaluating surface reconstruction from point clouds of open scenes / Yanis Marchand (2022)  

Permalink

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

Permalink