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Determination of the under water position of objects by reflectorless total stations / Štefan Rákay in Survey review, vol 53 n°376 (January 2021)
[article]
Titre : Determination of the under water position of objects by reflectorless total stations Type de document : Article/Communication Auteurs : Štefan Rákay, Auteur ; Slavomír Labant, Auteur ; Karol Bartoš, Auteur Année de publication : 2021 Article en page(s) : pp 35 - 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bathymétrie
[Termes IGN] bathymétrie laser
[Termes IGN] erreur géométrique
[Termes IGN] lever bathymétrique
[Termes IGN] mesurage électronique de distances
[Termes IGN] objet
[Termes IGN] onde électromagnétique
[Termes IGN] réfraction de l'eau
[Termes IGN] scène sous-marine
[Termes IGN] tachéomètre électronique
[Termes IGN] vitesseRésumé : (auteur) When surveying through a water surface, a distortion of several centimetres caused by the refraction and the change in the velocity of the electromagnetic waves can be observed. Therefore, neither the position nor the height of an underwater point (object), which can be seen from above the water surface, is correctly measured. The authors want to point out the magnitude of geometric errors when measuring to points under water as well as the computation of correct under water positions of points from measurement through a water layer. A practical experiment was performed for a water depth of 0.16 m. Numéro de notice : A2021-048 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1683488 Date de publication en ligne : 03/11/2019 En ligne : https://doi.org/10.1080/00396265.2019.1683488 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96783
in Survey review > vol 53 n°376 (January 2021) . - pp 35 - 43[article]
Titre : Developing graphics frameworks with Python and OpenGL Type de document : Guide/Manuel Auteurs : Lee Stemkoski, Auteur ; Michael Pascale, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2021 Importance : 345 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-00-318137-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] image 3D
[Termes IGN] interface de programmation
[Termes IGN] OpenGL
[Termes IGN] processeur graphique
[Termes IGN] programmation informatique
[Termes IGN] Python (langage de programmation)
[Termes IGN] scène 3D
[Termes IGN] texture d'image
[Termes IGN] transformation géométriqueRésumé : (éditeur) Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds. You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results. Note de contenu : 1- Introduction to computer graphics
2- Introduction to Pygame and OpenGL
3- Matrix algebra and transformations
4- A scene graph framework
5- Textures
6- Light and shadowNuméro de notice : 28306 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel DOI : 10.1201/9781003181378 En ligne : https://doi.org/10.1201/9781003181378 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98077
Titre : Efficiently distributed watertight surface reconstruction Type de document : Article/Communication Auteurs : Laurent Caraffa , Auteur ; Yanis Marchand , Auteur ; Mathieu Brédif , Auteur ; Bruno Vallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 1-Pas de projet / Conférence : 3DV 2021, International Conference on 3D Vision 01/12/2021 03/12/2021 Londres online Royaume-Uni Proceedings IEEE Importance : pp 1432 - 1441 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme Graph-Cut
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] scène
[Termes IGN] semis de points
[Termes IGN] Spark
[Termes IGN] triangulation de DelaunayRésumé : (auteur) We present an out-of-core and distributed surface reconstruction algorithm which scales efficiently on arbitrarily large point clouds (with optical centres) and produces a 3D watertight triangle mesh representing the surface of the underlying scene. Surface reconstruction from a point cloud is a difficult problem and existing state of the art approaches are usually based on complex pipelines making use of global algorithms (i.e. Delaunay triangulation, graph-cut optimisation). For one of these approaches, we investigate the distribution of all the steps (in particular Delaunay triangulation and graph-cut optimisation) in order to propose a fully scalable method. We show that the problem can be tiled and distributed across a cloud or a cluster of PCs by paying a careful attention to the interactions between tiles and using Spark computing framework. We confirm the efficiency of this approach with an in-depth quantitative evaluation and the successful reconstruction of a surface from a very large data set which combines more than 350 million aerial and terrestrial LiDAR points. Numéro de notice : C2021-037 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/3DV53792.2021.00150 En ligne : https://doi.org/10.1109/3DV53792.2021.00150 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99167
Titre : Evaluating surface mesh reconstruction of open scenes Type de document : Article/Communication Auteurs : Yanis Marchand , Auteur ; Bruno Vallet , Auteur ; Laurent Caraffa , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 369 - 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] code source libre
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] évaluation
[Termes IGN] qualité du processus
[Termes IGN] reconstruction d'objet
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (auteur) This paper addresses the evaluation of algorithms reconstructing a watertight surface from a point cloud acquired on an open scene. The objective is to set a rigorous protocol measuring the quality of the reconstruction and to propose a quality metric that is informative with respect to the various qualities that such an algorithm should have, and in particular its capacity to interpolate and extrapolate accurately. Our approach aims at being more informative and rigorous than previous works on this topic. In addition, we use publicly available data and our implementation is open-source. We argue that a rigorous evaluation of surface reconstruction of open scenes needs to be performed on synthetic data where a perfect continuous ground truth surface is available, so we developed our own LiDAR simulator of which we give a description in the present paper. Numéro de notice : C2021-014 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2021-369-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-369-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98065
Titre : Geometric approximation of structured scenes from images Type de document : Thèse/HDR Auteurs : Muxingzi Li, Auteur ; Renaud Marlet, Directeur de la recherche Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 122 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat résentée en vue de l’obtention du grade de docteur en Informatique de l’Université Côte d’AzurLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approximation
[Termes IGN] chaîne de traitement
[Termes IGN] détection d'objet
[Termes IGN] extraction automatique
[Termes IGN] maillage
[Termes IGN] modélisation 3D
[Termes IGN] primitive géométrique
[Termes IGN] recalage de données localisées
[Termes IGN] reconstruction d'image
[Termes IGN] scène urbaine
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] superposition de données
[Termes IGN] vectorisation
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Geometric approximation of urban objects with compact and accurate representation is a challenging problem that concerns both computer vision and computer graphics communities. Existing literature mainly focuses on reconstruction from high-quality point clouds obtained by laser scanning which are too costly for many practical scenarios. This motivates the investigation into the problem of geometric approximation from low-budget image data. Dense reconstruction from a collection of images is made possible by recent advances in multi-view stereo techniques, yet the resulting point cloud is often far from perfect for generating a compact model. In particular, our goal is to describe the captured scene with a compact and accurate representation. In this thesis, we propose two generic algorithms which address different aspects of image-based geometric approximation. First, we present an algorithm for extracting and vectorizing objects in images with polygons. Second, we present a global registration algorithm from multi-modal geometric data, typically 3D point clouds and surface meshes. Both approaches exploit detection of linear geometric primitives to approximate either 2D silhouettes with polygons consisting of line segments, or 3D point sets with a collection of planar shapes. The proposed algorithms could be used sequentially to form a pipeline for geometric approximation of an urban object from a set of image data, consisting of an overhead shot for coarse model extraction and multi-view stereo data for point cloud generation. We demonstrate the robustness and scalability of our methods for structured scenes and objects, as well as applicative potential for free-form objects. Note de contenu : 1- Introduction
2- Literature review
3- Polygonal image segmentation
4- 3D registration of multi-modal geometry
5- Application to floor modeling
6- Conclusion and perspectivesNuméro de notice : 28627 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Côte d'Azur : 2021 Organisme de stage : INRIA DOI : sans En ligne : https://tel.hal.science/tel-03388295v2/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99557 Geometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)PermalinkHolographic SAR tomography 3-D reconstruction based on iterative adaptive approach and generalized likelihood ratio test / Dong Feng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkPlanimetric simplification and lexicographic optimal chains for 3D urban scene reconstruction / Julien Vuillamy (2021)PermalinkRemote sensing and GIS / Basudeb Bhatta (2021)PermalinkRendu basé image d'images historiques / Maria Scarlleth Gomes de Castro (2021)PermalinkMS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds / Haifeng Luo in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkParsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkUnderstanding the role of individual units in a deep neural network / David Bau in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 48 (1 December 2020)PermalinkVisualization of 3D property data and assessment of the impact of rendering attributes / Stefan Seipel in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)PermalinkWeighted spherical sampling of point clouds for forested scenes / Alex Fafard in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 10 (October 2020)Permalink