Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > reconstruction 3D > reconstruction d'objet
reconstruction d'objetSynonyme(s)reconstruction de surface |
Documents disponibles dans cette catégorie (375)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection / Anandakumar M. Ramiya in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)
[article]
Titre : Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection Type de document : Article/Communication Auteurs : Anandakumar M. Ramiya, Auteur ; Rama Rao Nidamanuri, Auteur ; R. Krishnan, Auteur Année de publication : 2016 Article en page(s) : pp 121 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classificateur
[Termes IGN] détection de partie cachée
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image multibande
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de pointsRésumé : (Auteur) The urban land cover mapping and automated extraction of building boundaries is a crucial step in generating three-dimensional city models. This study proposes an object-based point cloud labelling technique to semantically label light detection and ranging (LiDAR) data captured over an urban scene. Spectral data from multispectral images are also used to complement the geometrical information from LiDAR data. Initial object primitives are created using a modified colour-based region growing technique. Multiple classifier system is then applied on the features extracted from the segments for classification and also for reducing the subjectivity involved in the selection of classifier and improving the precision of the results. The proposed methodology produces two outputs: (i) urban land cover classes and (ii) buildings masks which are further reconstructed and vectorized into three-dimensional buildings footprints. Experiments carried out on three airborne LiDAR datasets show that the proposed technique successfully discriminates urban land covers and detect urban buildings. Numéro de notice : A2016-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1034195 Date de publication en ligne : 06/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1034195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80001
in Geocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 121 - 139[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2016011 RAB Revue Centre de documentation En réserve L003 Disponible Photogrammetric computer vision / Wolfgang Förstner (2016)
Titre : Photogrammetric computer vision : statistics, geometry, orientation and reconstruction Type de document : Guide/Manuel Auteurs : Wolfgang Förstner, Auteur ; Bernhard P. Wrobel, Auteur Editeur : Springer Nature Année de publication : 2016 Collection : Geometry and computing, ISSN 1866-6795 num. 11 Importance : 816 p. Format : 21 x 28 cm ISBN/ISSN/EAN : 978-3-319-11549-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] aérotriangulation numérique
[Termes IGN] compensation par faisceaux
[Termes IGN] couple stéréoscopique
[Termes IGN] données maillées
[Termes IGN] données vectorielles
[Termes IGN] estimation statistique
[Termes IGN] géométrie
[Termes IGN] géométrie projective
[Termes IGN] image 2D
[Termes IGN] image 3D
[Termes IGN] incertitude géométrique
[Termes IGN] ligne (géométrie)
[Termes IGN] modèle de Gauss-Markov
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] plan (géométrie)
[Termes IGN] point
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] rotation d'objet
[Termes IGN] semis de points
[Termes IGN] transformation géométrique
[Termes IGN] variable aléatoire
[Termes IGN] vision par ordinateur
[Termes IGN] visualisation 3DIndex. décimale : 33.30 Photogrammétrie numérique Résumé : (Editeur) This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their relations, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is devoted to modelling the geometry of single and multiple cameras, addressing calibration and orientation, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computation problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exercises, and they are supported by an appendix that explains the basic mathematical notation and a detailed index. The book can serve as the basis for undergraduate and graduate courses in photogrammetry, computer vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods. Note de contenu : 1. Introduction
1.1. Tasks for Photogrammetric Computer Vision
1.2. Modelling in Photogrammetric Computer Vision
1.3. The Book
1.4. On Notation
Part One - Statistics and Estimation
2. Probability Theory and Random Variables
2.1. Notions of Probability
2.2. Axiomatic Definition of Probability
2.3. Random Variables
2.4. Distributions
2.5. Moments
2.6. Quantiles of a Distribution
2.7. Functions of Random Variables
2.8. Stochastic Processes
2.9. Generating Random Numbers
2.10. Exercises
3. Testing
3.1. Principles of Hypothesis Testing
3.2. Testability of an Alternative Hypothesis
3.3. Common Tests
3.4. Exercises
4. Estimation
4.1. Estimation Theory
4.2. The Linear Gauss–Markov Model
4.3. Gauss–Markov Model with Constraints
4.4. The Nonlinear Gauss–Markov Model
4.5. Datum or Gauge Definitions and Transformations
4.6. Evaluation
4.7. Robust Estimation and Outlier Detection
4.8. Estimation with Implicit Functional Models
4.9. Methods for Closed Form Estimations
4.10. Estimation in Autoregressive Models
4.11. Exercises
Part two - Geometry
5. Homogeneous Representations of Points, Lines and Planes
5.1. Homogeneous Vectors and Matrices
5.2. Homogeneous Representations of Points and Lines in 2D
5.3. Homogeneous Representations in IPn
5.4. Homogeneous Representations of 3D Lines
5.5. On Plücker Coordinates for Points, Lines and Planes
5.6. The Principle of Duality
5.7. Conics and Quadrics
5.8. Normalizations of Homogeneous Vectors
5.9. Canonical Elements of Coordinate Systems
5.10. Exercises
6. Transformations
6.1. Structure of Projective Collineations
6.2. Basic Transformations
6.3. Concatenation and Inversion of Transformations
6.4. Invariants of Projective Mappings
6.5. Perspective Collineations
6.6. Projective Correlations
6.7. Hierarchy of Projective Transformations and Their Characteristics
6.8. Normalizations of Transformations
6.9. Conditioning
6.10. Exercises
7. Geometric Operations
7.1. Geometric Operations in 2D Space
7.2. Geometric Operations in 3D Space
7.3. Vector and Matrix Representations for Geometric Entities
7.4. Minimal Solutions for Conics and Transformations
7.5. Exercises
8. Rotations
8.1. Rotations in 3D
8.2. Concatenation of Rotations
8.3. Relations Between the Representations for Rotations
8.4. Rotations from Corresponding Vector Pairs
8.5. Exercises
9. Oriented Projective Geometry
9.1. Oriented Entities and Constructions
9.2. Transformation of Oriented Entities
9.3. Exercises
10. Reasoning with Uncertain Geometric Entities
10.1. Motivation
10.2. Representing Uncertain Geometric Elements
10.3. Propagation of the Uncertainty of Homogeneous Entities
10.4. Evaluating Statistically Uncertain Relations
10.5. Closed Form Solutions for Estimating Geometric Entities
10.6. Iterative Solutions for Maximum Likelihood Estimation
10.7. Exercises
Part Three - Orientation and Reconstruction
11. Overview
11.1. Scene, Camera, and Image Models
11.2. The Setup of Orientation, Calibration, and Reconstruction
11.3. Exercises
12. Geometry and Orientation of the Single Image
12.1. Geometry of the Single Image
12.2. Orientation of the Single Image
12.3. Inverse Perspective and 3D Information from a Single Image
12.4. Exercises
13. Geometry and Orientation of the Image Pair
13.1. Motivation
13.2 The Geometry of the Image Pair
13.3 Relative Orientation of the Image Pair
13.4. Triangulation
13.5. Absolute Orientation and Spatial Similarity Transformation
13.6. Orientation of the Image Pair and Its Quality
13.7. Exercises
14. Geometry and Orientation of the Image Triplet
14.1. Geometry of the Image Triplet
14.2. Relative Orientation of the Image Triplet
14.3. Exercises
15. Bundle Adjustment
15.1. Motivation for Bundle Adjustment and Its Tasks
15.2. Block Adjustment
15.3. Sparsity of Matrices, Free Adjustment and Theoretical Precision
15.4. Self-calibrating Bundle Adjustment
15.5. Camera Calibration
15.6. Outlier Detection and Approximate Values
15.7. View Planning
15.8. Exercises
16. Surface Reconstruction
16.1. Introduction
16.2. Parametric 21/2D Surfaces
16.3. Models for Reconstructing One-Dimensional Surface Profiles
16.4. Reconstruction of 21/2D Surfaces from 3D Point Clouds
16.5. Examples for Surface Reconstruction
16.6. Exercises
Appendix: Basics and Useful Relations from Linear AlgebraNuméro de notice : 22610 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82915 Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 22610-01 33.30 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible 22610-02 DEP-ECP Livre Marne-la-Vallée Dépôt en unité Exclu du prêt 22610-03 DEP-ELZ Livre Marne-la-Vallée Dépôt en unité Exclu du prêt Application of technical measures and software in constructing photorealistic 3D models of historical building using ground-based and aerial (UAV) digital images / Aleksander Zarnowski in Reports on geodesy and geoinformatics, vol 99 (December 2015)
[article]
Titre : Application of technical measures and software in constructing photorealistic 3D models of historical building using ground-based and aerial (UAV) digital images Type de document : Article/Communication Auteurs : Aleksander Zarnowski, Auteur ; Anna Banaszek, Auteur ; Sebastian Banaszek, Auteur Année de publication : 2015 Article en page(s) : pp 54 - 63 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] chambre non métrique
[Termes IGN] drone
[Termes IGN] image aérienne
[Termes IGN] intégration de données
[Termes IGN] modélisation 3D
[Termes IGN] monument historique
[Termes IGN] patrimoine immobilier
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] rendu réalisteRésumé : (auteur) Preparing digital documentation of historical buildings is a form of protecting cultural heritage. Recently there have been several intensive studies using non-metric digital images to construct realistic 3D models of historical buildings. Increasingly often, non-metric digital images are obtained with unmanned aerial vehicles (UAV). Technologies and methods of UAV flights are quite different from traditional photogrammetric approaches. The lack of technical guidelines for using drones inhibits the process of implementing new methods of data acquisition.
This paper presents the results of experiments in the use of digital images in the construction of photo-realistic 3D model of a historical building (Raphaelsohns’ Sawmill in Olsztyn). The aim of the study at the first stage was to determine the meteorological and technical conditions for the acquisition of aerial and ground-based photographs. At the next stage, the technology of 3D modelling was developed using only ground-based or only aerial non-metric digital images. At the last stage of the study, an experiment was conducted to assess the possibility of 3D modelling with the comprehensive use of aerial (UAV) and ground-based digital photographs in terms of their labour intensity and precision of development. Data integration and automatic photo-realistic 3D construction of the models was done with Pix4Dmapper and Agisoft PhotoScan software Analyses have shown that when certain parameters established in an experiment are kept, the process of developing the stock-taking documentation for a historical building moves from the standards of analogue to digital technology with considerably reduced cost.Numéro de notice : A2015--024 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.2478/rgg-2015-0012 En ligne : https://doi.org/10.2478/rgg-2015-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80848
in Reports on geodesy and geoinformatics > vol 99 (December 2015) . - pp 54 - 63[article]Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry / Roeland Boeters in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)
[article]
Titre : Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry Type de document : Article/Communication Auteurs : Roeland Boeters, Auteur ; Ken Arroyo Ohori, Auteur ; Filip Biljecki, Auteur ; Sisi Zlatanova, Auteur Année de publication : 2015 Article en page(s) : pp 2248 - 2268 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] CityGML
[Termes IGN] géométrie
[Termes IGN] levé urbain
[Termes IGN] niveau de détail
[Termes IGN] positionnement en intérieur
[Termes IGN] reconstruction 3D du bâtiRésumé : (Auteur) The international standard CityGML defines five levels of detail (LODs) for 3D city models, but only the highest of these (LOD4) supports modelling the indoor geometry of a building, which must be acquired in correspondingly high detail and therefore at a high cost. Whereas simple 3D city models of the exterior of buildings (e.g. CityGML LOD2) can be generated largely automatically, and are thus now widely available and have a great variety of applications, similarly simple models containing their indoor geometries are rare.
In this paper we present two contributions: (i) the definition of a level of detail LOD2+, which extends the CityGML LOD2 specification with indoor building geometries of comparable complexity to their exterior geometries in LOD2; and more importantly (ii) a method for automatically generating such indoor geometries based on existing CityGML LOD2 exterior geometries. We validate our method by generating LOD2+ models for a subset of the Rotterdam 3D data set and visually comparing these models to their real counterparts in building blueprints and imagery from Google Street View and Bing Maps. Furthermore, we use the LOD2+ models to compute the net internal area of each dwelling and validate our results by comparing these values to the ones registered in official government data sets.Numéro de notice : A2015-624 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1072201 En ligne : https://doi.org/10.1080/13658816.2015.1072201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78093
in International journal of geographical information science IJGIS > vol 29 n° 12 (December 2015) . - pp 2248 - 2268[article]Creation of parametric BIM objects from point clouds using nurbs / Luigi Barazzetti in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)
[article]
Titre : Creation of parametric BIM objects from point clouds using nurbs Type de document : Article/Communication Auteurs : Luigi Barazzetti, Auteur ; Fabrizio Banfi, Auteur ; Raffaella Brumana, Auteur ; Mattia Previtali, Auteur Année de publication : 2015 Article en page(s) : pp 339 - 362 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] architecture
[Termes IGN] fonction spline
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de points
[Termes IGN] surface B-spline rationnelle non uniformeRésumé : (auteur) This paper presents an innovative procedure to create parametric building information modelling (BIM) objects from point clouds of complex architectural features. BIM technology requires an advanced parametric representation of the geometry involving spatial relationships, constraints and material properties. The aim of the procedure is a BIM-based reconstruction methodology that preserves the level of detail encapsulated in photogrammetric and laser-scanning point clouds, and relies on non-uniform rational basis splines (NURBS) curves. The particular case of architectural objects with irregular shapes is addressed due to the lack of commercial BIM software able to handle such buildings. An actual case study made up of 7·5 billion points is discussed to demonstrate the use of the proposed procedure with huge point-cloud datasets. Numéro de notice : A2015-826 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12122 Date de publication en ligne : 15/12/2015 En ligne : https://doi.org/10.1111/phor.12122 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79122
in Photogrammetric record > vol 30 n° 152 (December 2015 - February 2016) . - pp 339 - 362[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 106-2015041 RAB Revue Centre de documentation En réserve L003 Disponible Le relevé 3D du patrimoine culturel : la Ca' Vendramin dei Leoni, musée Guggenheim de Venise / Caterina Balletti in XYZ, n° 145 (décembre 2015 - février 2016)PermalinkStochastic geometrical model and Monte Carlo optimization methods for building reconstruction from InSAR data / Yue Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkStreetgen: in-base procedural-based road generation / Rémi Cura in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W5 (October 2015)PermalinkCalibration of a camera–projector monochromatic system / Cristina Portales in Photogrammetric record, vol 30 n° 149 (March - May 2015)PermalinkPermalinkAutomatic construction of 3-D building model from airborne LIDAR data through 2-D snake algorithm / Jianhua Yan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkPermalinkConception d’une méthode de consolidation de grands réseaux lasergrammétriques / Emmanuel Clédat (2015)PermalinkModélisation 3D de la chapelle Saint-Laurent et de la place du Château (secteur 3) pour extraction de données archéologiques et visite virtuelle / Robin Bruna (2015)PermalinkPermalink