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Termes IGN > sciences naturelles > physique > traitement d'image > reconstruction 3D
reconstruction 3DSynonyme(s)reconstruction volumique reconstruction volumique tridimensionnelle |
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Three-dimensional reconstruction of fluvial surface sedimentology and topography using personal mobile laser scanning / Richard David Williams in Earth surface processes and landforms, vol 45 n° 1 (January 2020)
[article]
Titre : Three-dimensional reconstruction of fluvial surface sedimentology and topography using personal mobile laser scanning Type de document : Article/Communication Auteurs : Richard David Williams, Auteur ; Marie-Lou Lamy , Auteur ; Georgios Maniatis, Auteur ; Eilidh Stott, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 251 - 261 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] instrumentation Leica
[Termes IGN] modèle numérique de surface
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] rugosité
[Termes IGN] sédimentRésumé : (auteur) High-resolution quantification of fluvial topography has been enabled by a number of geomatics technologies. Hyperscale surveys with spatial extents of Numéro de notice : A2020-877 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1002/esp.4747 Date de publication en ligne : 27/10/2019 En ligne : https://doi.org/10.1002/esp.4747 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99684
in Earth surface processes and landforms > vol 45 n° 1 (January 2020) . - pp 251 - 261[article]Underwater calibration in near real time: Focus on detection optimized by AI and selection of calibration patterns / Loïca Avanthey (2020)
Titre : Underwater calibration in near real time: Focus on detection optimized by AI and selection of calibration patterns Type de document : Article/Communication Auteurs : Loïca Avanthey, Auteur ; Laurent Beaudoin, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Conférence : IGARSS 2020, 2020 IEEE International Geoscience and Remote Sensing Symposium 26/09/2020 02/10/2020 Waikoloa, Hawaï Etats-Unis proceedings IEEE Importance : n° 9324519 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] capteur imageur
[Termes IGN] couple stéréoscopique
[Termes IGN] dioptre
[Termes IGN] étalonnage
[Termes IGN] reconstruction 3D
[Termes IGN] scène sous-marineRésumé : (auteur) The 3D reconstruction of underwater scenes from pairs of stereoscopic images requires to model the sensor. To take into account the refraction in the environment and to be compatible with the operational field constraints, we use the compensated pinhole model (the diopter is considered as an additional lens) whose parameters are estimated by an in situ calibration with a pattern. In this article, we propose an optimization by AI of the pattern detection to have real-time feedback and a process which selects on the fly the shots allowing to improve the estimation quality of the model so the manipulation can be stopped when sufficient number of relevant images has been reached. We present the results obtained on a database made up of 60,000 images taken in swimming pools and at sea. Numéro de notice : C2020-039 Affiliation des auteurs : IGN (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS39084.2020.9324519 Date de publication en ligne : 17/02/2021 En ligne : https://doi.org/10.1109/IGARSS39084.2020.9324519 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102625 Underwater field equipment of a network of landmarks optimized for automatic detection by AI / Laurent Beaudoin (2020)
Titre : Underwater field equipment of a network of landmarks optimized for automatic detection by AI Type de document : Article/Communication Auteurs : Laurent Beaudoin, Auteur ; Loïca Avanthey, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Conférence : IGARSS 2020, 2020 IEEE International Geoscience and Remote Sensing Symposium 26/09/2020 02/10/2020 Waikoloa, Hawaï Etats-Unis proceedings IEEE Importance : n° 9323589 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] détection automatique
[Termes IGN] détection de cible
[Termes IGN] point d'appui
[Termes IGN] reconstruction 3DRésumé : (auteur) To qualify the point clouds obtained by 3D reconstruction of a global study area in close-range remote sensing, control points, whose position has been measured essentially manually in the field with an instrument whose precision is known, are used. In the underwater environment, equipping the field and carrying out these measurements is a complex operation to perform due to the peculiarities of the environment. We present in this article a first step towards the automation of this task, the automatic detection of targets by a deep learning algorithm which will serve to correctly position the control points locally, and a simplification of the manual measurement which will serve in future work to control the results of automatic readings. Numéro de notice : C2020-040 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS39084.2020.9323589 En ligne : https://doi.org/10.1109/IGARSS39084.2020.9323589 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102626 A versatile and efficient data fusion methodology for heterogeneous airborne LiDAR and optical imagery data acquired under unconstrained conditions / Thanh Huy Nguyen (2020)
Titre : A versatile and efficient data fusion methodology for heterogeneous airborne LiDAR and optical imagery data acquired under unconstrained conditions Type de document : Thèse/HDR Auteurs : Thanh Huy Nguyen, Auteur ; Jean-Marc Le Caillec, Directeur de thèse ; Sylvie Daniel, Directeur de thèse Editeur : Institut Mines-Télécom Atlantique IMT Atlantique Année de publication : 2020 Autre Editeur : Québec : Université Laval Importance : 173 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Ecole Nationale Supérieure des Mines-Telecom Atlantique Bretagne Pays de la Loire-IMT Atlantique, Spécialité : Signal, Image, VisionLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données
[Termes IGN] image optique
[Termes IGN] recalage de données localisées
[Termes IGN] reconstruction 3D
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The necessity and importance of representing a scene in 3-D have been exemplified through numerous remote sensing applications, such as urban planning, disaster management, etc. In these applications, LiDAR and optical imagery data have been used extensively. A complementarity existing between airborne LiDAR and aerial/satellite optical imagery datasets motivates the fusion between them, allowing to represent the observed scenes in 3-D with a better precision and completeness. In recent years, automatic building footprint extraction in urban and residential scenes has become a subject of growing interest among the field of 3-D scene representation and reconstruction. With the rising availability of massive amount of data captured by different LiDAR and imagery sensors onboard airborne and spaceborne platforms, new opportunities arise to perform this task on a large scale. However, existing fusion methods generally consider either hybrid acquisition systems consisting of LiDAR and optical cameras rigidly fixed, or datasets acquired from the same platform at identical or very close dates, and having the same spatial resolution. They do not intend to cope with datasets collected from different platforms with different acquisition configuration at different moments, having different spatial resolutions and levels of detail. Such a context is referred to as unconstrained acquisition context. Furthermore, extracting buildings on a large scale is a complex task. Existing methods reported over the years have achieved relatively significant results by assuming building shapes, enforcing geometrical constraints, or limiting on specific urban areas. Such assumptions are no longer applicable when dealing with large-scale datasets. This research work is devoted to the development of a versatile coarse-to-fine registration method between airborne LiDAR and aerial/satellite optical imagery datasets collected in an unsconstrained acquisition context. It aims at overcoming the challenges associated with this context such as the spatial shift between the datasets, the differences of spatial resolution and level of detail, etc. In addition, this research work elaborates an efficient building footprint extraction method, providing a high accuracy level while being an unsupervised method dedicated to largescale applications. The proposed method, called Super-Resolution-based Snake Model (SRSM), consists in an adaptation of snake models—a conventional image segmentation technique—to operate on high-resolution LiDAR-based elevation images generated by a super-resolution process. It pertains the unconstrained data acquisition context, serving as a prime application example. Relevant results have been achieved when rigorously assessing the proposed methods, namely a highly desirable accuracy level compared to existing methods. Note de contenu : Introduction
1- State of the art
2- Coarse-to-fine Registration of Airborne LiDAR and Optical Imagery Data on Urban Scenes
3- Building Extraction Based on the Fusion of Airborne LiDAR and Optical Imagery Data
4- Conclusions and PerspectivesNuméro de notice : 28327 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences Géomatiques : Mines-Télécom Atlantique : 2020 Organisme de stage : Lab-STICC DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03123328/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98401 Introducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)
[article]
Titre : Introducing spatial regularization in SAR tomography reconstruction Type de document : Article/Communication Auteurs : Clément Rambour, Auteur ; Loïc Denis, Auteur ; Florence Tupin, Auteur ; Hélène Oriot, Auteur Année de publication : 2019 Article en page(s) : pp 8600 - 8617 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] acquisition comprimée
[Termes IGN] analyse spectrale
[Termes IGN] écho radar
[Termes IGN] fractionnement
[Termes IGN] image à très haute résolution
[Termes IGN] image radar moirée
[Termes IGN] image TerraSAR-X
[Termes IGN] mécanique de Lagrange
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] scène urbaine
[Termes IGN] TerraSAR-X
[Termes IGN] tomographie radarRésumé : (auteur) The resolution achieved by current synthetic aperture radar (SAR) sensors provides a detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building facade, and roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: 1) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing) and 2) transformation from radar geometry to ground geometry and extraction of significant scatterers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale nonconvex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate, on simulated data and a TerraSAR-X tomographic data set, the potential of this approach to produce 3-D reconstructions of urban surfaces. Numéro de notice : A2019-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921756 Date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94588
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8600 - 8617[article]Pré-localisation des données pour la modélisation 3D de tunnels : développements et évaluations / Christophe Heinkelé in Revue Française de Photogrammétrie et de Télédétection, n° 221 (novembre 2019)PermalinkAnalysis of free image-based modelling systems applied to support topographic measurements / José Miguel Caldera-Cordero in Survey review, vol 51 n° 367 (July 2019)PermalinkRoofN3D: a database for 3D building reconstruction with deep learning / Andreas Wichmann in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)PermalinkAutomatic reconstruction of fully volumetric 3D building models from oriented point clouds / Sebastian Ochmann in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkAlbedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkGeometric comparison and quality evaluation of 3D models of indoor environments / H. Tran in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkComplete 3D scene parsing from an RGBD image / Chuhang Zou in International journal of computer vision, vol 127 n° 2 (February 2019)PermalinkEquivalent constraints for two-view geometry : Pose solution/pure rotation identification and 3D reconstruction / Qi Cai in International journal of computer vision, vol 127 n° 2 (February 2019)PermalinkRepeated structure detection for 3D reconstruction of building façade from mobile lidar data / Yanming Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)PermalinkPermalink