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Geometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)  

Public Titre : Geometric computer vision: omnidirectional visual and remotely sensed data analysis Type de document : Thèse/HDR Auteurs : Pouria Babahajiani, Auteur ; Moncef Gabbouj, Directeur de thèse Editeur : Tampere [Finlande] : Tampere University Année de publication : 2021 Importance : 147 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-952-03-1979-3 Note générale : bibliographie Accademic Dissertation, Tampere University, Faculty of Information Technology and Communication Sciences Finland Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] chaîne de traitement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] effet de profondeur cinétique [Termes IGN] espace public [Termes IGN] extraction de traits caractéristiques [Termes IGN] image panoramique [Termes IGN] image Streetview [Termes IGN] image terrestre [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle sémantique de données [Termes IGN] réalité virtuelle [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] vision par ordinateur [Termes IGN] zone urbaine Index. décimale : THESE Thèses et HDR Résumé : (auteur) Information about the surrounding environment perceived by the human eye is one of the most important cues enabled by sight. The scientific community has put a great effort throughout time to develop methods for scene acquisition and scene understanding using computer vision techniques. The goal of this thesis is to study geometry in computer vision and its applications. In computer vision, geometry describes the topological structure of the environment. Specifically, it concerns measures such as shape, volume, depth, pose, disparity, motion, and optical flow, all of which are essential cues in scene acquisition and understanding. This thesis focuses on two primary objectives. The first is to assess the feasibility of creating semantic models of urban areas and public spaces using geometrical features coming from LiDAR sensors. The second objective is to develop a practical Virtual Reality (VR) video representation that supports 6-Degrees-of-Freedom (DoF) head motion parallax using geometric computer vision and machine learning. The thesis’s first contribution is the proposal of semantic segmentation of the 3D LiDAR point cloud and its applications. The ever-growing demand for reliable mapping data, especially in urban environments, has motivated mobile mapping systems’ development. These systems acquire high precision data and, in particular 3D LiDAR point clouds and optical images. A large amount of data and their diversity make data processing a complex task. A complete urban map data processing pipeline has been developed, which annotates 3D LiDAR points with semantic labels. The proposed method is made efficient by combining fast rule-based processing for building and street surface segmentation and super-voxel-based feature extraction and classification for the remaining map elements (cars, pedestrians, trees, and traffic signs). Based on the experiments, the rule-based processing stage provides substantial improvement not only in computational time but also in classification accuracy. Furthermore, two back ends are developed for semantically labeled data that exemplify two important applications: (1) 3D high definition urban map that reconstructs a realistic 3D model using input labeled point cloud, and (2) semantic segmentation of 2D street view images. The second contribution of the thesis is the development of a practical, fast, and robust method to create high-resolution Depth-Augmented Stereo Panoramas (DASP) from a 360-degree VR camera. A novel and complete optical flow-based pipeline is developed, which provides stereo 360-views of a real-world scene with DASP. The system consists of a texture and depth panorama for each eye. A bi-directional flow estimation network is explicitly designed for stitching and stereo depth estimation, which yields state-of-the-art results with a limited run-time budget. The proposed architecture explicitly leverages geometry by getting both optical flow ground-truths. Building architectures that use this knowledge simplifies the learning problem. Moreover, a 6-DoF testbed for immersive content quality assessment is proposed. Modern machine learning techniques have been used to design the proposed architectures addressing many core computer vision problems by exploiting the enriched information coming from 3D scene structures. The architectures proposed in this thesis are practical systems that impact today’s technologies, including autonomous vehicles, virtual reality, augmented reality, robots, and smart-city infrastructures. Note de contenu : 1- Introduction 2- Geometry in Computer Vision 3- Contributions 4- Conclusion Numéro de notice : 28323 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Computing and Electrical Engineering : Tempere, Finland : 2021 DOI : sans En ligne : https://trepo.tuni.fi/handle/10024/131379 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98342

Urban 3D segmentation and modelling from street view images and LiDAR point clouds / Pouria Babahajiani in Machine Vision and Applications, sans n° ([01/06/2017])  

Public [article]  inMachine Vision and Applications > sans n° [01/06/2017] Titre : Urban 3D segmentation and modelling from street view images and LiDAR point clouds Type de document : Article/Communication Auteurs : Pouria Babahajiani, Auteur ; Lixin Fan, Auteur ; Joni-Kristian Kämäräinen, Auteur ; Moncef Gabbouj, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] base de données urbaines [Termes IGN] cartographie urbaine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] façade [Termes IGN] image terrestre [Termes IGN] milieu urbain [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) 3D urban maps with semantic labels and metric information are not only essential for the next generation robots such autonomous vehicles and city drones, but also help to visualize and augment local environment in mobile user applications. The machine vision challenge is to generate accurate urban maps from existing data with minimal manual annotation. In this work, we propose a novel methodology that takes GPS registered LiDAR (Light Detection And Ranging) point clouds and street view images as inputs and creates semantic labels for the 3D points clouds using a hybrid of rule-based parsing and learning-based labelling that combine point cloud and photometric features. The rule-based parsing boosts segmentation of simple and large structures such as street surfaces and building facades that span almost 75% of the point cloud data. For more complex structures, such as cars, trees and pedestrians, we adopt boosted decision trees that exploit both structure (LiDAR) and photometric (street view) features. We provide qualitative examples of our methodology in 3D visualization where we construct parametric graphical models from labelled data and in 2D image segmentation where 3D labels are back projected to the street view images. In quantitative evaluation we report classification accuracy and computing times and compare results to competing methods with three popular databases: NAVTEQ True, Paris-Rue-Madame and TLS (terrestrial laser scanned) Velodyne. Numéro de notice : A2017-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00138-017-0845-3 En ligne : https://doi.org/10.1007/s00138-017-0845-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85269 [article]