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3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)

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Titre :	3D urban scene understanding by analysis of LiDAR, color and hyperspectral data
Type de document :	Thèse/HDR
Auteurs :	David Duque-Arias, Auteur ; Beatriz Marcotegui, Directeur de thèse ; Jean-Emmanuel Deschaud, Directeur de thèse
Editeur :	Paris : Université Paris Sciences et Lettres
Année de publication :	2021
Importance :	191 p.
Format :	21 x 30 cm
Note générale :	bibliographie Thèse de Doctorat de l'Université PSL, Spécialité : Morphologie Mathématique
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse de scène 3D [Termes IGN] apprentissage profond [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] graphe [Termes IGN] image hyperspectrale [Termes IGN] image optique [Termes IGN] modélisation géométrique de prise de vue [Termes IGN] monde virtuel [Termes IGN] morphologie mathématique [Termes IGN] navigation autonome [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] traitement d'image
Index. décimale :	THESE Thèses et HDR
Résumé :	(auteur) Point clouds have attracted the interest of the research community over the last years. Initially, they were mostly used for remote sensing applications. More recently, thanks to the development of low-cost sensors and the publication of some open source libraries, they have become very popular and have been applied to a wider range of applications. One of them is the autonomous vehicle where many efforts have been made in the last century to make it real. A very important bottleneck nowadays for the autonomous vehicle is the evaluation of the proposed algorithms. Due to the huge number of possible scenarios, it is not feasible to perform it in real life. An alternative is to simulate virtual environments where all possible configurations can be set up beforehand. However, they are not as realistic as the real world is. In this thesis, we studied the pertinence of including hyperspectral images in the creation of new virtual environments. Furthermore, we proposed new methods to improve 3D scene understanding for autonomous vehicles. During this research, we addressed the following topics. Firstly, we analyzed the spectrum in color and hyperspectral images because it provides a description about the electromagnetic radiation at different frequencies. Some applications rely only on visible colors. In other cases, such as the characterization of materials, the study of the invisible range is required. For this purpose, we proposed a simplified spectrum representation that preserves its diversity, the Graph-based color lines (GCL) model. Secondly, we studied the integration of hyperspectral images, color images and point clouds in urban scenes. The analysis was carried out by using the data acquired during this thesis in the context of the REPLICA project FUI 24. We inspected spectral signatures of different objects and reflectance histograms of the images. The obtained results demonstrate that urban scenes are challenging scenarios for current technology of hyperspectral cameras due to the presence of uncontrolled light conditions and moving actors. Thirdly, we worked with 3D point clouds from urban scenes that have proved to be a reliable type of data, much less sensitive to illumination variations than cameras. They are more accurate than color images and permit to obtain precise 3D models of urban environments. Deep learning techniques are very popular in this domain. A key element of these techniques is the loss function that drives the optimization process. We proposed two new loss functions to perform semantic segmentation tasks: power Jaccard loss and hierarchical loss. They obtained a higher performance in evaluated scenarios than classical losses not only in 3D point clouds but also in color and gray scale images. Moreover, we proposed a new dataset (Paris Carla 3D Dataset) composed of synthetic and real point clouds from urban scenes. It is expected to be used by the research community for different automatic tasks such as semantic segmentation, instance segmentation and scene completion. Finally, we conducted a detailed analysis of the influence of RGB features in semantic segmentation of urban point clouds. We compared several training scenarios and identified that color systematically improves the performance in certain classes. It demonstrates that including a more detailed description of the spectrum, when the hyperspectral cameras technology increases its sensitivity, can be useful to improve scene description of urban scenes.
Note de contenu :	1- Introduction 2- Data used in this thesis 3- Graph based color lines (GCL) 4- Study of REPLICA data 5- Power Jaccard losses for semantic segmentation 6- Segmentation of point clouds 7- Conclusions and perspectives
Numéro de notice :	28464
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE/MATHEMATIQUE/URBANISME
Nature :	Thèse française
Note de thèse :	Thèse de Doctorat : Morphologie Mathématique : Paris sciences et lettres : 2021
Organisme de stage :	Centre de Morphologie Mathématique
DOI :	sans
En ligne :	https://pastel.hal.science/tel-03434199/
Format de la ressource électronique :	URL
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=99076

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Centre de documentationscientifique

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Sélection de la langue

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L'actu ! Horaires du CDOS

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IGN / ENSG

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2014 -2024 IGN

Centre de documentation
scientifique