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Titre : Deep learning based 3D reconstruction: supervision and representation Type de document : Thèse/HDR Auteurs : François Darmon, Auteur ; Pascal Monasse, Directeur de thèse ; Mathieu Aubry, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2022 Importance : 115 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat de l'Ecole des Ponts ParisTech, spécialité informatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] carte de profondeur
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction
[Termes IGN] géométrie épipolaire
[Termes IGN] maillage
[Termes IGN] modèle stéréoscopique
[Termes IGN] point d'intérêt
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motion
[Termes IGN] voxelIndex. décimale : THESE Thèses et HDR Résumé : (auteur) 3D reconstruction is a long standing problem in computer vision. Yet, state-of-the-art methods still struggle when the images used have large illumination changes, many occlusions or limited textures. Deep Learning holds promises of improving 3D reconstruction in such setups, but classical methods still produce the best results. In this thesis we analyse the specificity of deep learning applied to multiview 3D reconstruction and introduce new deep learning based methods.The first contribution of this thesis is an analysis of the possible supervision for training Deep Learning models for sparse image matching. We introduce a two-step algorithm that first computes low resolution matches using deep learning and then matches classical local features inside the matches regions. We analyze several levels of supervision and show that our new epipolar supervision leads to the best results.The second contribution is also a study of supervision for Deep Learning but applied to another scenario: calibrated 3D reconstruction in the wild. We show that existing unsupervised methods do not work on such data and we introduce a new training technique that solves this issue. We then exhaustively compare unsupervised approach and supervised approaches with different network architectures and training data.Finally, our third contribution is about data representation. Neural implicit representation were recently used for image rendering. We adapt this representation to the multiview reconstruction problem and we introduce a new method that, similar to classical 3D reconstruction techniques, optimizes photo-consistency between projections of multiple images. Our approach outperforms state-of-the-art by a large margin. Note de contenu : 1- Introduction
2- Background
3- Deep learning for guiding keypoint matching
4- Deep Learning based Multi-View Stereo in the wild
5- Multi-view reconstruction with implicit surfaces and patch warping
6- ConclusionNuméro de notice : 24085 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Ponts ParisTech : 2022 Organisme de stage : Laboratoire d'Informatique Gaspard-Monge LIGM DOI : sans En ligne : https://www.theses.fr/2022ENPC0024 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102473
Titre : Deep learning based vehicle detection in aerial imagery Type de document : Monographie Auteurs : Lars Wilko Sommer, Éditeur scientifique Editeur : Karlsruhe [Allemagne] : KIT Scientific Publishing Année de publication : 2022 Importance : 276 p. Format : 15 x 21 cm ISBN/ISSN/EAN : 978-3-7315-1113-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] ancre
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre
[Termes IGN] image aérienne
[Termes IGN] véhiculeRésumé : (éditeur) This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced. Note de contenu : 1- Introduction
2- Related work
3- Concept
4- Experimental setup
5- Base framework
6- Integration of contextual knowledge
7- Runtime optimization
8- Evaluation
9- Conclusions and outlookNuméro de notice : 28685 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5445/KSP/1000135415 En ligne : https://doi.org/10.5445/KSP/1000135415 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100015
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 / Conférence : ISPRS 2022, Commission 1, 24th 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 donnéesRésumé : (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 Detection of windthrown tree stems on UAV-orthomosaics using U-Net convolutional networks / Stefan Reder in Remote sensing, vol 14 n° 1 (January-1 2022)
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Titre : Detection of windthrown tree stems on UAV-orthomosaics using U-Net convolutional networks Type de document : Article/Communication Auteurs : Stefan Reder, Auteur ; J.P. Mund, Auteur ; Nicole Albert, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] branche (arbre)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] dommage forestier causé par facteurs naturels
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image captée par drone
[Termes IGN] orthophotoplan numérique
[Termes IGN] segmentation sémantique
[Termes IGN] tempête
[Termes IGN] troncRésumé : (auteur) The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amount of damaged wood can be obtained quickly and included in the planning of clearance measures. The present work utilizes UAV-orthophotos and an adaptation of the U-Net architecture for the semantic segmentation and localization of windthrown stems. The network was pre-trained with generic datasets, randomly combining stems and background samples in a copy–paste augmentation, and afterwards trained with a specific dataset of a particular windthrow. The models pre-trained with generic datasets containing 10, 50 and 100 augmentations per annotated windthrown stems achieved F1-scores of 73.9% (S1Mod10), 74.3% (S1Mod50) and 75.6% (S1Mod100), outperforming the baseline model (F1-score 72.6%), which was not pre-trained. These results emphasize the applicability of the method to correctly identify windthrown trees and suggest the collection of training samples from other tree species and windthrow areas to improve the ability to generalize. Further enhancements of the network architecture are considered to improve the classification performance and to minimize the calculative costs. Numéro de notice : A2022-082 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14010075 En ligne : https://doi.org/10.3390/rs14010075 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99476
in Remote sensing > vol 14 n° 1 (January-1 2022) . - n° 75[article]
Titre : Development of object detectors for satellite images by deep learning Type de document : Mémoire Auteurs : Alissa Kouraeva, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 57 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire d'ingénieur 3e année, Cycle PPMD Photogrammétrie, Positionnement et Mesure de DéformationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] angle d'incidence
[Termes IGN] apprentissage profond
[Termes IGN] détection du bâti
[Termes IGN] image aérienne
[Termes IGN] image Pléiades-HR
[Termes IGN] image Pléiades-Neo
[Termes IGN] jeu de données
[Termes IGN] OpenStreetMap
[Termes IGN] réalité de terrain
[Termes IGN] recalage d'imageMots-clés libres : Frame Field Learning algorithm Index. décimale : MPPMD Mémoires du mastère spécialisé Photogrammétrie, Positionnement et Mesures de Déformation Résumé : (auteur) With various uses cases in different sectors - marine, cartography, defense - object detection in satellite images is at the heart of image processing methods. This study aims to test existing building detection algorithms and improve them with the final goal being a precise cartography of buildings for 3D reconstruction with a high level of details. The Polygonization by Frame Field Learning algorithm is tested on different types of images: aerial images (50cm resolution), satellite images with 50cm (Pleiades) and 30cm (Pleiades Neo) resolutions. The ground truth is either already provided (Digitanie) or has to be retrieved from open access databases (OSM or BD TOPO IGN). Some problems of ground truth overlap appear in Pleiades neo images due to the relative precision in positioning of different data and also due to the incidence angle, that provides a greater revisiting capability. A re-implementation of the Frame Field Learning algorithm with the PyTorch Lightning framework is done in this study, with different experiments conducted concerning the configuration of the algorithm. Note de contenu : Introduction
1- Data
2- Methods
3- Results and discussion
ConclusionNuméro de notice : 24052 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Airbus Defence and Space Geo SA Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101926 Effective triplet mining improves training of multi-scale pooled CNN for image retrieval / Federico Vaccaro in Machine Vision and Applications, vol 33 n° 1 (January 2022)
PermalinkÉléments pour l'analyse et le traitement d'images : application à l'estimation de la qualité du bois / Rémy Decelle (2022)
PermalinkPermalinkPermalinkExploring data fusion for multi-object detection for intelligent transportation systems using deep learning / Amira Mimouna (2022)
PermalinkGénération d’un jeu de données d’entraînement et mise en oeuvre d’une architecture de détection par deep learning des numéros de parcelles sur les plans du cadastre Napoléonien / Tiecoumba Ibrahim Tamela (2022)
PermalinkHistograms of oriented mosaic gradients for snapshot spectral image description / Lulu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)
PermalinkHistorical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India / M. Dhananjayan in Marine geodesy, vol 45 n° 1 (January 2022)
PermalinkImproving LSMA for impervious surface estimation in an urban area / Jin Wang in European journal of remote sensing, vol 55 n° 1 (2022)
PermalinkMapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine / Jiyu Liu in Geomatics, Natural Hazards and Risk, vol 13 (2022)
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