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Fast calculation of gravitational effects using tesseroids with a polynomial density of arbitrary degree in depth / Fang Ouyang in Journal of geodesy, vol 96 n° 12 (December 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 12 (December 2022) . - n° 97 Titre : Fast calculation of gravitational effects using tesseroids with a polynomial density of arbitrary degree in depth Type de document : Article/Communication Auteurs : Fang Ouyang, Auteur ; Long-wei Chen, Auteur ; Zhi-gang Shao, Auteur Année de publication : 2022 Article en page(s) : n° 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] champ de gravitation [Termes IGN] coordonnées sphériques [Termes IGN] discrétisation [Termes IGN] intégrale de Newton [Termes IGN] inversion [Termes IGN] quadrature [Termes IGN] tesseroid [Termes IGN] transformation rapide de Fourier Résumé : (auteur) Fast and accurate calculation of gravitational effects on a regional or global scale with complex density environment is a critical issue in gravitational forward modelling. Most existing significant developments with tessroid-based modelling are limited to homogeneous density models or polynomial ones of a limited order. Moreover, the total gravitational effects of tesseroids are often calculated by pure summation in these methods, which makes the calculation extremely time-consuming. A new efficient and accurate method based on tesseroids with a polynomial density up to an arbitrary order in depth is developed for 3D large-scale gravitational forward modelling. The method divides the source region into a number of tesseroids, and the density in each tesseroid is assumed to be a polynomial function of arbitrary degree. To guarantee the computational accuracy and efficiency, two key points are involved: (1) the volume Newton’s integral is decomposed into a one-dimensional integral with a polynomial density in the radial direction, for which a simple analytical recursive formula is derived for efficient calculation, and a surface integral over the horizontal directions evaluated by the Gauss–Legendre quadrature (GLQ) combined with a 2D adaptive discretization strategy; (2) a fast and flexible discrete convolution algorithm based on 1D fast Fourier transform (FFT) and a general Toepritz form of weight coefficient matrices is adopted in the longitudinal dimension to speed up the computation of the cumulative contributions from all tesseroids. Numerical examples show that the gravitational fields predicted by the new method have a good agreement with the corresponding analytical solutions for spherical shell models with both polynomial and non-polynomial density variations in depth. Compared with the 3D GLQ methods, the new algorithm is computationally more accurate and efficient. The calculation time is significantly reduced by 3 orders of magnitude as compared with the traditional 3D GLQ methods. Application of the new algorithm in the global crustal CRUST1.0 model further verifies its reliability and practicability in real cases. The proposed method will provide a powerful numerical tool for large-scale gravity modelling and also an efficient forward engine for inversion and continuation problems. Numéro de notice : A2022-896 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01688-9 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01688-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102248 [article]

DiffusionNet: discretization agnostic learning on surfaces / Nicholas Sharp in ACM Transactions on Graphics, TOG, Vol 41 n° 3 (June 2022)  

Public [article]  inACM Transactions on Graphics, TOG > Vol 41 n° 3 (June 2022) . - pp 1 - 16 Titre : DiffusionNet: discretization agnostic learning on surfaces Type de document : Article/Communication Auteurs : Nicholas Sharp, Auteur ; Souhaib Attaiki, Auteur ; K. Crane, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] discrétisation [Termes IGN] maillage [Termes IGN] Perceptron multicouche [Termes IGN] semis de points [Termes IGN] Triangular Regular Network [Termes IGN] voisinage (relation topologique) Résumé : (auteur) We introduce a new general-purpose approach to deep learning on three-dimensional surfaces based on the insight that a simple diffusion layer is highly effective for spatial communication. The resulting networks are automatically robust to changes in resolution and sampling of a surface—a basic property that is crucial for practical applications. Our networks can be discretized on various geometric representations, such as triangle meshes or point clouds, and can even be trained on one representation and then applied to another. We optimize the spatial support of diffusion as a continuous network parameter ranging from purely local to totally global, removing the burden of manually choosing neighborhood sizes. The only other ingredients in the method are a multi-layer perceptron applied independently at each point and spatial gradient features to support directional filters. The resulting networks are simple, robust, and efficient. Here, we focus primarily on triangle mesh surfaces and demonstrate state-of-the-art results for a variety of tasks, including surface classification, segmentation, and non-rigid correspondence. Numéro de notice : A2022-321 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1145/3507905 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1145/3507905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100369 [article]

A framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2021 (July 2021) . - pp 61 - 68 Titre : A framework to manage uncertainty in the computation of waste collection routes after a flood Type de document : Article/Communication Auteurs : Arnaud Le Guilcher , Auteur ; Sofiane Martel, Auteur ; Mickaël Brasebin , Auteur ; Yann Méneroux , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 4, 24th ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 4 Article en page(s) : pp 61 - 68 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] cadre conceptuel [Termes IGN] calcul d'itinéraire [Termes IGN] catastrophe naturelle [Termes IGN] collecte des déchets [Termes IGN] discrétisation spatiale [Termes IGN] incertitude géométrique [Termes IGN] inondation [Termes IGN] programmation stochastique [Termes IGN] variable aléatoire Résumé : (auteur) In this paper, we describe a framework to find a good quality waste collection tour after a flood, without having to solve a complicated optimization problem from scratch in limited time. We model the computation of a waste collection tour as a capacitated routing problem, on the vertices or on the edges of a graph, with uncertain waste quantities and uncertain road availability. Multiple models have been conceived to manage uncertainty in routing problems, and we build on the ideas of discretizing the uncertain parameters and computing master solutions that can be adapted to propose an original method to compute efficient solutions. We first introduce our model for the progressive removal of the uncertainty, then outline our method to compute solutions: our method first considers a low-dimensional set of random variables that govern the behaviour of the problem parameters, discretizes these variables and computes a solution for each discrete point before the flood, and then uses these solutions as a basis to build operational solutions when there are enough information about the parameters of the routing problem. We then give computational tools to implement this method. We give a framework to compute the basis of solutions in an efficient way, by computing all the solutions simultaneously and sharing information (that can lead to good quality solutions) between the different problems based on how close their parameters are, and we also describe how real solutions can be derived from this basis. Our main contributions are our model for the progressive removal of uncertainty, our multi-step method to compute efficient solutions, and our intrusive framework to compute solutions on the discrete grid of parameters. Numéro de notice : A2021-316 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2021-61-2021 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2021-61-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97946 [article]

From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)  

Public Titre : From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction Type de document : Thèse/HDR Auteurs : Audrey Richard, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2021 Note générale : bibliographie A thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] chaîne de traitement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] démonstration de faisabilité [Termes IGN] discrétisation spatiale [Termes IGN] jeu de données localisées [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modélisation sémantique [Termes IGN] optimisation (mathématiques) [Termes IGN] Pays-Bas [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] Rhénanie du Nord-Wesphalie (Allemagne) [Termes IGN] semis de points [Termes IGN] texturage [Termes IGN] Zurich (Suisse) Résumé : (auteur) Capturing automatically a virtual 3D model of an object or a scene from a collection of images is a useful capability with a wide range of applications, including virtual/augmented reality, heritage preservation, consumer digital entertainment, autonomous robotics, navigation, industrial vision or metrology, and many more. Since the early days of photogrammetry and computer vision, it has been a topic of intensive research but has eluded a general solution for it. 3D modeling requires more than reconstructing a cloud of 3D points from images; it requires a high-fidelity representation whose form is often dependent on individual objects. This thesis guides you in the journey of image-based 3D reconstruction through several advanced methods that aims to push its boundaries, from precise and complete geometry to detailed appearance, using both theory with elegant mathematics and more recent breakthroughs in deep learning. To evaluate these methods, thorough experiments are conducted at scene level (and large-scale) where efficiency is of key importance, and at object level where accuracy, completeness and photorealism can be better appreciated. To show the individual potential of each of these methods, as well as the possible wide coverage in terms of applications, different scenarios are considered and serve as a proof-of-concept. Thereby, the journey starts with large-scale city modeling using aerial photography from the cities of Zürich (Switzerland), Enschede (Netherlands) and Dortmund (Germany), followed by single object completion using the synthetic dataset ShapeNet, that includes objects like cars, benches or planes that can be found in every city, to finish with the embellishment of these digital models via high-resolution texture mapping using a multi-view 3D dataset of real and synthetic objects, like for example statues and fountains that also dress the landscape of cities. Combining them together into an incremental pipeline dedicated to a specific application would require further tailoring but is quite possible. Numéro de notice : 17650 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD : Sciences : ETH Zurich : 2021 En ligne : http://dx.doi.org/10.3929/ethz-b-000461735 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97892

Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)  

Public [article]  inComputer Vision and image understanding > vol 171 (June 2018) . - pp 1 - 9 Titre : Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data Type de document : Article/Communication Auteurs : Michalis A. Savelonas, Auteur ; Ioannis Pratikakis, Auteur ; Theoharis Theoharis, Auteur ; Georgios Thanellas, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 1 - 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] analyse de sensibilité [Termes IGN] analyse spatiale [Termes IGN] classification barycentrique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] codage [Termes IGN] détection de piéton [Termes IGN] discrétisation spatiale [Termes IGN] distribution de Fisher [Termes IGN] données lidar [Termes IGN] échantillonnage de données [Termes IGN] image à basse résolution [Termes IGN] reconnaissance de formes Résumé : (auteur) Range-based pedestrian recognition is instrumental towards the development of autonomous driving and driving assistance systems. This work introduces encoding methods for pedestrian recognition, based on statistical shape analysis of 3D LIDAR data. The proposed approach has two variants, based on the encoding of local shape descriptors either in a spatially agnostic or spatially sensitive fashion. The latter method derives more detailed cues, by enriching the ‘gross’ information reflected by overall statistics of local shape descriptors, with ‘fine-grained’ information reflected by statistics associated with spatial clusters. Experiments on artificial LIDAR datasets, which include challenging samples, as well as on a large scale dataset of real LIDAR data, lead to the conclusion that both variants of the proposed approach (i) obtain high recognition accuracy, (ii) are robust against low-resolution sampling, (iii) are robust against increasing distance, and (iv) are robust against non-standard shapes and poses. On the other hand, the spatially-sensitive variant is more robust against partial occlusion and bad clustering. Numéro de notice : A2018-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cviu.2018.06.001 Date de publication en ligne : 15/06/2018 En ligne : https://www.sciencedirect.com/science/article/pii/S1077314218300766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92439 [article]

Numerical solution to the oblique derivative boundary value problem on non-uniform grids above the Earth topography / Matej Medl’a in Journal of geodesy, vol 92 n° 1 (January 2018)  

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Global, dense multiscale reconstruction for a billion points / Benjamin Ummenhofer in International journal of computer vision, vol 125 n° 1-3 (December 2017)  

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The confirmed realities and myths about the benefits and costs of 3D Visualization and virtual reality in discrete event modeling and simulation: A descriptive meta-analysis of evidence from research and practice / Ikpe Justice Akpan in Computers & Industrial Engineering, vol 112 (October 2017)  

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Local and global evaluation for remote sensing image segmentation / Tengfei Su in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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ViCTOr : paysage virtuel pour explorer les dynamiques de la VIticulture et de la Consommation en TouRaine / Etienne Delay in Cybergeo, European journal of geography, n° 2017 ([01/06/2017])  

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Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data / André Dittrich in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)  

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mR-V: Line simplification through mnemonic rasterization / Emmanuel Stefanakis in Geomatica [en ligne], vol 70 n° 4 (December 2016)  

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MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images / Ilias Grinias in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)  

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Developing predictive models of wind damage in Austrian forests / Ferenc Pasztor in Annals of Forest Science, vol 72 n° 3 (May 2015)  

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An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds / C. Cabo in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)  

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