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Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation / Bing Liu in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 4 (July 2021) . - pp 305 - 319 Titre : Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation Type de document : Article/Communication Auteurs : Bing Liu, Auteur ; Linfang Ding, Auteur ; Liqiu Meng, Auteur Année de publication : 2021 Article en page(s) : pp 305 - 319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] conception orientée utilisateur [Termes IGN] GPS assisté pour la navigation (technologies) [Termes IGN] hologramme [Termes IGN] information sémantique [Termes IGN] navigation virtuelle [Termes IGN] point de repère [Termes IGN] positionnement en intérieur [Termes IGN] questionnaire [Termes IGN] réalité mixte [Termes IGN] téléphone intelligent [Termes IGN] utilisateur civil Résumé : (auteur) Landmarks are essential and widely used in human navigation. However, many indoor environments lack visually salient landmarks, which leads to difficulties in navigating in and learning complex and similar-looking indoor environments. In this study, we designed and implemented virtual semantic landmarks in Mixed Reality (MR)-based indoor environments and conducted a user study to explore whether such landmarks can assist spatial knowledge acquisition during navigation. More specifically, we employed the untethered, head-mounted mixed reality device Microsoft HoloLens and used iconic holograms to show the semantic landmarks. In the user study, we used sketch map, landmark locating tasks and interview to assess the results of the spatial knowledge acquisition and collect advice on improving the MR-based navigation interface. The results show that virtual semantic landmarks can assist the acquisition of corresponding knowledge, as such landmarks were labeled second most often in landmark locating task. In addition, individual cases show that head-mounted mixed reality devices may influence not only vision, but also height or time perception of certain users. Our result can be applied to facilitate the design of MR-based navigation interfaces and assist spatial knowledge acquisition. Numéro de notice : A2021-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1908171 date de publication en ligne : 22/04/2021 En ligne : https://doi.org/10.1080/15230406.2021.1908171 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97852 [article]

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Three-dimensional reconstruction of single input image based on point cloud / Yu Hou in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 7 (July 2021) . - pp 479 - 484 Titre : Three-dimensional reconstruction of single input image based on point cloud Type de document : Article/Communication Auteurs : Yu Hou, Auteur ; Ruifeng Zhai, Auteur ; Xueyan Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 479 - 484 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] balayage laser [Termes IGN] classification par réseau neuronal [Termes IGN] données localisées 3D [Termes IGN] jeu de données localisées [Termes IGN] navigation autonome [Termes IGN] réalité virtuelle [Termes IGN] reconstruction 3D [Termes IGN] semis de points Résumé : (Auteur) Three-dimensional reconstruction from a single image has excellent future prospects. The use of neural networks for three-dimensional reconstruction has achieved remarkable results. Most of the current point-cloud-based three-dimensional reconstruction networks are trained using nonreal data sets and do not have good generalizability. Based on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago ()data set of large-scale scenes, this article proposes a method for processing real data sets. The data set produced in this work can better train our network model and realize point cloud reconstruction based on a single picture of the real world. Finally, the constructed point cloud data correspond well to the corresponding three-dimensional shapes, and to a certain extent, the disadvantage of the uneven distribution of the point cloud data obtained by light detection and ranging scanning is overcome using the proposed method. Numéro de notice : A2021-570 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.479 date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.14358/PERS.87.7.479 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98162 [article]

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An automatic workflow for orientation of historical images with large radiometric and geometric differences / Ferdinand Maiwald in Photogrammetric record, vol 36 n° 174 (June 2021)  

Public [article]  inPhotogrammetric record > vol 36 n° 174 (June 2021) . - pp 77 - 103 Titre : An automatic workflow for orientation of historical images with large radiometric and geometric differences Type de document : Article/Communication Auteurs : Ferdinand Maiwald, Auteur ; Hans-Gerd Maas, Auteur Année de publication : 2021 Article en page(s) : pp 77 - 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement de formes [Termes IGN] artefact [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] image ancienne [Termes IGN] plus proche voisin (algorithme) [Termes IGN] réalité augmentée [Termes IGN] réalité virtuelle [Termes IGN] reconstruction 3D [Termes IGN] scène urbaine [Termes IGN] SIFT (algorithme) [Termes IGN] structure-from-motion Résumé : (auteur) This contribution proposes a workflow for a completely automatic orientation of historical terrestrial urban images. Automatic structure from motion (SfM) software packages often fail when applied to historical image pairs due to large radiometric and geometric differences causing challenges with feature extraction and reliable matching. As an innovative initialising step, the proposed method uses the neural network D2-Net for feature extraction and Lowe’s mutual nearest neighbour matcher. The principal distance for every camera is estimated using vanishing point detection. The results were compared to three state-of-the-art SfM workflows (Agisoft Metashape, Meshroom and COLMAP) with the proposed workflow outperforming the other SfM tools. The resulting camera orientation data are planned to be imported into a web and virtual/augmented reality (VR/AR) application for the purpose of knowledge transfer in cultural heritage. Numéro de notice : A2021-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12363 date de publication en ligne : 06/06/2021 En ligne : https://doi.org/10.1111/phor.12363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97925 [article]

Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change / Jiawei Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 6 (June 2021) . - pp 1155 - 1178 Titre : Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change Type de document : Article/Communication Auteurs : Jiawei Huang, Auteur ; Melissa S. Lucash, Auteur ; Robert M. Scheller, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1155 - 1178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière [Termes IGN] carte de la végétation [Termes IGN] changement climatique [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] monde virtuel [Termes IGN] paysage forestier [Termes IGN] réalité virtuelle [Termes IGN] visualisation 3D [Termes IGN] Wisconsin (Etats-Unis) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Communicating and understanding climate induced environmental changes can be challenging, especially using traditional representations such as graphs, maps or photos. Immersive visualizations and experiences offer an intuitive, visceral approach to otherwise rather abstract concepts, but creating them scientifically is challenging. In this paper, we linked ecological modeling, procedural modeling, and virtual reality to provide an immersive experience of a future forest. We mapped current tree species composition in northern Wisconsin using the Forest Inventory and Analysis (FIA) data and then forecast forest change 50 years into the future under two climate scenarios using LANDIS-II, a spatially-explicit, mechanistic simulation model. We converted the model output (e.g., tree biomass) into parameters required for 3D visualizations with analytical modeling. Procedural rules allowed us to efficiently and reproducibly translate the parameters into a simulated forest. Data visualization, environment exploration, and information retrieval were realized using the Unreal Engine. A system evaluation with experts in ecology provided positive feedback and future topics for a comprehensive ecosystem visualization and analysis approach. Our approach to create visceral experiences of forests under climate change can facilitate communication among experts, policy-makers, and the general public. Numéro de notice : A2021-384 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1830997 date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1830997 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97641 [article]

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

3D hand mesh reconstruction from a monocular RGB image / Hao Peng in The Visual Computer, vol 36 n° 10 - 12 (October 2020)  

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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

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Extended reality in spatial sciences: A review of research challenges and future directions / Arzu Çöltekin in ISPRS International journal of geo-information, vol 9 n° 7 (July 2020)  

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Interoperable information model for geovisualization and interaction in XR environments / Daeil Seo in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

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Comparative usability of an augmented reality sandtable and 3D GIS for education / Antoni B. Moore in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)  

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Geovisualization: multidimensional exploration of the territory / Sidonie Christophe (2020)  

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Mixed Reality and Three-Dimensional Computer Graphics / Branislav Sobota (2020)  

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Perspective switch and spatial knowledge acquisition: effects of age, mental rotation ability and visuospatial memory capacity on route learning in virtual environments with different levels of realism / Ismini E. Lokka in Cartography and Geographic Information Science, Vol 47 n° 1 (January 2020)  

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Smart cities and construction technologies / Kefeng Zhang (2020)  

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Towards interoperable research infrastructures for environmental and earth sciences / Zhiming Zhao (2020)  

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