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Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 17 - 28 Titre : Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Li Li, Auteur ; Nan Song, Auteur ; Fei Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] modélisation 3D [Termes IGN] Perceptron multicouche [Termes IGN] primitive géométrique [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] toit Résumé : (auteur) Three-dimensional (3D) building roof reconstruction from airborne LiDAR point clouds is an important task in photogrammetry and computer vision. To automatically reconstruct the 3D building models at Level of Detail 2 (LoD-2) from airborne LiDAR point clouds, the data-driven approaches usually need to be performed in two steps: geometric primitive extraction and roof structure inference. Obviously, the traditional approaches are not end-to-end, the accumulated errors in different stages cannot be avoided and the final 3D roof models may not be optimal. In addition, the results of 3D roof models largely depend on the accuracy of geometric primitives (planes, lines, etc.). To solve these problems, we present a deep learning-based approach to directly reconstruct building roofs from airborne LiDAR point clouds, named Point2Roof. In our method, we start by extracting the deep features for each input point using PointNet++. Then, we identify a set of candidate corner points from the input point clouds using the extracted deep features. In addition, we also regress the offset for each candidate corner point to refine their locations. After that, these candidates are clustered into a set of initial vertices, and we further refine their locations to obtain the final accurate vertices. Finally, we propose a Paired Point Attention (PPA) module to predict the true model edges from an exhaustive set of candidate edges between the vertices. Unlike traditional roof modeling approaches, the proposed Point2Roof is end-to-end. However, due to the lack of a building reconstruction dataset, we construct a large-scale synthetic dataset to verify the effectiveness and robustness of the proposed Point2Roof. The experimental results conducted on the synthetic benchmark demonstrate that the proposed Point2Roof significantly outperforms the traditional roof modeling approaches. The experiments also show that the network trained on the synthetic dataset can be applied to the real point clouds after fine-tuning the trained model on a small real dataset. The large-scale synthetic dataset, the small real dataset and the source code of our approach are publicly available in https://github.com/Li-Li-Whu/Point2Roof. Numéro de notice : A2022-745 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.027 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101728 [article]

City3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 9 (May-1 2022) . - n° 2254 Titre : City3D: Large-scale building reconstruction from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Jin Huang, Auteur ; Jantien E. Stoter, Auteur ; Ravi Peters, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] empreinte [Termes IGN] mur [Termes IGN] polygonale [Termes IGN] primitive géométrique [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] toit [Termes IGN] Triangular Regular Network [Termes IGN] triangulation de Delaunay Résumé : (auteur) We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne LiDAR point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne LiDAR point clouds and the use of 3D city models in urban applications. Numéro de notice : A2022-387 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.3390/rs14092254 Date de publication en ligne : 07/05/2022 En ligne : https://doi.org/10.3390/rs14092254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100667 [article]

Is map generalisation a computational cartography problem? / Guillaume Touya (2022)  

Public Titre : Is map generalisation a computational cartography problem? Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2022 Projets : LostInZoom / Touya, Guillaume Conférence : CompCarto 2022, 1st workshop on Computational Cartography 19/05/2022 20/05/2022 Bonn Allemagne programme Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] généralisation cartographique automatisée [Termes IGN] géomètrie algorithmique [Vedettes matières IGN] Généralisation Résumé : (auteur) Map generalisation is one of the processes of map design, when the spatial data used to make the map is too detailed for the scale of the map. Map generalisation seeks to abstract and simplify this detailed spatial information to represent it on the map with a good legibility. Map generalisation involves different types of transformations: selection, simplification, displacement, aggregation, collapse, etc. Historically, computational geometry researchers greatly contributed to the automation of map generalisation by proposing algorithms to achieve some of these atomic transformations. But the research questions addressed by researchers on map generalisation have changed over the years, with the progress on the automation, and the changes in the way people use maps (apparition of multi-scale zoomable web maps). Is map generalisation still a computational cartography problem? To discuss this rhetorical question, this presentation illustrates the current issues of map generalisation, with past contribution of computational cartography and possible computational cartography problems. For instance, there are still needs for new specific algorithms, either because maps are much more diverse, or because the larger scale range calls for new transformations. As maps tend to mix elements of topographic maps and elements of schematized maps, computational cartography is also relevant to solve this task. Finally, as the current trend in map generalisation is the use of deep learning models, the presentation discusses the relevance of computational cartography techniques in this context. Numéro de notice : C2022-028 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Conférence invitée nature-HAL : Conf-Invitée DOI : sans En ligne : https://hal.archives-ouvertes.fr/IGN-ENSG/hal-03677309v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100959

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

Public Titre : Registration of heterogenous data for urban modeling Type de document : Thèse/HDR Auteurs : Rahima Djahel, Auteur ; Pascal Monasse, Directeur de thèse ; Bruno Vallet , Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2022 Projets : BIOM / Vallet, Bruno Importance : 160 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse soutenue pour obtenir le grade de Docteur à l'École des Ponts ParisTech, spécialité Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données hétérogènes [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] espace intérieur [Termes IGN] état de l'art [Termes IGN] extraction de traits caractéristiques [Termes IGN] jeu de données localisées [Termes IGN] méthode robuste [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] primitive géométrique [Termes IGN] Ransac (algorithme) [Termes IGN] recalage d'image [Termes IGN] scène urbaine [Termes IGN] segment de droite Index. décimale : THESE Thèses et HDR Résumé : (Auteur) Cette thèse fait partie du projet Modelisation Intérieur/Extérieur de Bâtiments (BIOM) qui vise à la modélisation automatique et simultanée de l’intérieur et de l’extérieur de bâtiments à partir de données hétérogènes. L'hétérogénéité est à la fois dans le type de données (image et Light Detection and Ranging (LiDAR)) et la plate-forme d'acquisition: acquisition terrestre intérieure/extérieure ou aérienne. Le premier enjeu d'une telle modélisation est donc de recaler précisément ces données. Les travaux menés ont confirmé que l'environnement et le type de données conditionnent le choix de l'algorithme de recalage. Notre contribution consiste à exploiter les propriétés fondamentales des données et des plateformes d'acquisition afin de proposer des solutions potentielles à tous les problèmes de recalage rencontrés par le projet. Comme dans un environnement de bâtiments la plupart des objets sont composés de primitives géométriques (polygones planaires, lignes droites, ouvertures), nous avons choisi d'introduire des algorithmes de recalage reposant sur ces primitives. L'idée de base de ces algorithmes consiste en la définition d'une énergie globale entre les primitives extraites à partir des jeux de données à recaler et la proposition d'une méthode robuste pour optimiser cette énergie basée sur le paradigme RANSAC. Notre contribution va de la proposition de méthodes robustes pour extraire les primitives sélectionnées à l'intégration de ces primitives dans un cadre de recalage efficace. Nos solutions ont dépassé les limites des algorithmes existants et ont prouvé leur efficacité pour résoudre les problèmes rencontrés par le projet, tels que le recalage intérieur/extérieur, le recalage d'image/LiDAR et le recalage aérien/terrestre. Note de contenu : 1. Context and research problem 1.1 Introduction 1.2 BIOM project 1.3 Objectives 1.4 Building Information Modeling 1.5 Registration problem 1.6 Images registration 1.7 Point clouds registration 1.8 Contributions 1.9 Thesis outline 1.10 Publication List 2. Data description 2.1 Introduction 2.2 Image data 2.3 LiDAR data 2.4 Conclusion 3. Primitives detection 3.1 Introduction 3.2 Classification of primitives extraction methods 3.3 Performance evaluation 3.4 Planar polygons extraction 3.5 3D line segment detection from LIDAR data 3.6 3D lines segments detection and reconstruction from image data 3.7 Openings detection 3.8 Conclusion 4. Indoor/Outdoor Registration 4.1 Introduction 4.2 State of the art 4.3 Data 4.4 Planar polygons based registration 4.5 Openings based registration 4.6 Hybrid solution 4.7 Conclusion 5. Image/LiDAR data Registration 104 5.1 Introduction 5.2 State of the art 5.3 Overview and contributions 5.4 3D Segment Extraction 5.5 3D segments based registration 5.6 Iterative Closest Line (ICL) 5.7 Evaluation and discussion 5.8 Conclusion 6. Aerial/Terrestrial registration 6.1 Introduction 6.2 State of the art 6.3 3D segment extraction from heterogeneous image data 6.4 3D segments based algorithm adaptation 6.5 Evaluation and discussion 6.6 Conclusion 7. Conclusion 7.1 Contributions 7.2 Future work Appendices A. Implementation B. MLSD Improvement Numéro de notice : 26842 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : ENPC : 2022 Organisme de stage : Laboratoire d'Informatique Gaspard-Monge LIGM nature-HAL : Thèse DOI : sans Date de publication en ligne : 30/08/2022 En ligne : https://pastel.archives-ouvertes.fr/tel-03764907/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101526

Robustness of visual SLAM techniques to light changing conditions / Xi Wang (2022)  

Public Titre : Robustness of visual SLAM techniques to light changing conditions : Influence of contrasted local features, multi-planar representations and multimodal image analysis Type de document : Thèse/HDR Auteurs : Xi Wang, Auteur ; Eric Marchand, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2022 Importance : 153 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université de Rennes 1, Spécialité Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] cartographie et localisation simultanées [Termes IGN] éclairage [Termes IGN] estimation de pose [Termes IGN] information sémantique [Termes IGN] primitive géométrique [Termes IGN] programmation linéaire [Termes IGN] robotique [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (auteur) The SLAM (Simultaneous Localization And Mapping) technique concentrates on localizing and recovering the environment in a simultaneous way and is one of the core functionalities of many industrial products such as augmented reality, where the device poses should be tracked in real-time; autonomous driving, where one needs to localize the vehicle in a pre-generated map or unknown environment; and even modern filmmaking workflow, where the relative camera position and orientation are critical for post-processing or real-time prevising for directors and actors to visualise the visual effects on the stage. Multiple difficulties in different levels can influence the final performance of robot agents’s SLAM task, as the pipeline is long and complicated from the real world physics to the required information such as agent poses and 3-D map, which help us visualize colourful graphics scenes in AR devices or make hard decisions on the highway for autonomous driving. Many solutions are proposed for addressing each problem, respectively, with the means from classic statistic probability models to the modern data-driven deep neural network. However, the quest of improving the robot’s robustness under dynamic and complicated environments perisists and becomes more and more significant and active for nowadays robotics research. The need for improving the robustness of robot agents is imminent and regarded as one of most imperative factors for deploying robots ubiquitously in our daily life. Under this context, this thesis tries to address a small drop in the ocean of the problem of SLAM robustness, yet in a very systematic view: we try to break down the SLAM system into different and inter-influential modules. Then use the concept of "divide and conquer" for answering possible questions within each module and wishing to contribute to the community and help improve the robustness of SLAM systems under complicated conditions. With the above objectives, the contributions of the thesis are stated as follows for tackling the robustness problem from multiple angles: 1) From the image feature angle, we proposed a multiple layered image structure for improving the performance of traditional local image features under extreme conditions. Furthermore, an optimization method on linear searching and mutual information assisted convex optimization are designed for tuning the optimal parameters with the proposed structure; 2) From the geometric primitive angle, we proposed a relative pose estimation and SLAM framework under the multiple planar assumption, by keypoint feature-based and template tracker based methods, respectively. We tried to achieve better performance of mapping and tracking simultaneously with the help of a more general planar assumption. 3) From the angle of relocalization of the SLAM system, the idea is to recover the already passed locations of the robot agent for lowering the overall estimation error or when the robot is in lost status. We proposed a binary graph structure for embedding spatial information and heterogeneous data formats such as depth image, semantic information etc. The proposed method enables robotics SLAM systems to relocalize themselves with a higher success rate even under different lighting, weather and seasonal conditions. Note de contenu : 1- Introduction 2- Résumé 3- Background on visual SLAM techniques 4- Related work 5- Organisation 6- Multiple layers image 7- Multi-planar relative pose estimation via superpixel 8- TT-SLAM 9- Binary graph descriptor for robust relocalization on heterogeneous data Conclusion Numéro de notice : 24074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Rennes 1 : 2022 Organisme de stage : IRISA DOI : sans En ligne : https://www.theses.fr/2022REN1S022 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102162

Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation / Hamza Ben Addou in Géomatique expert, n° 136 (novembre - décembre 2021)

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A topology-preserving simplification method for 3D building models / Biao Wang in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)  

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Curved buildings reconstruction from airborne LiDAR data by matching and deforming geometric primitives / Jingwei Song in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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Geometric approximation of structured scenes from images / Muxingzi Li (2021)  

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Relation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds / Yuan Li in Remote sensing, vol 13 n° 1 (January-1 2021)  

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Cartographic generalization / Monika Sester in Journal of Spatial Information Science (JoSIS), n° 21 (2020)  

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Group diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Provably consistent distributed Delaunay triangulation / Mathieu Brédif in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Object-based incremental registration of terrestrial point clouds in an urban environment / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

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Des images satellites aux cartes vectorielles / Onur Tasar (2020)  

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