Centre de documentation
scientifique

EuroSDR project Commission 2, Mobile mapping - road environment mapping using mobile laser scanning / Harri Kaartinen (2013)

Public contenu dans EuroSDR-Project, Commission 1 and Commission 2 / Eija Honkavaara (March 2013)     Titre : EuroSDR project Commission 2, Mobile mapping - road environment mapping using mobile laser scanning : final report Type de document : Chapitre/Contribution Auteurs : Harri Kaartinen, Auteur ; Juha Hyyppä, Auteur ; Antero Kukko, Auteur ; Matti Lehtomäki, Auteur ; Anttoni Jaakkola, Auteur ; M. George Vosselman, Auteur ; Sander J. Oude Elberink, Auteur ; Martin Rutzinger, Auteur ; Shi Pu, Auteur ; Matti Vaaja, Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2013 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] base de données routières [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] lasergrammétrie [Termes IGN] système de numérisation mobile [Termes IGN] télémétrie laser Note de contenu : 1. Introduction 2. State-of-the-art in mobile laser scanning 3. Benchmarking of mobile laser scanning systems on a test field 4. Benchmarking of pole detection algorithms 5. Discusion ans conclusions Numéro de notice : H2013-009 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Chapître / contribution Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75593

Recognizing basic structures from mobile laser scanning data for road inventory studies / Shi Pu in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 66 n° 6 supplement (December 2011) . - pp 28 - 39 Titre : Recognizing basic structures from mobile laser scanning data for road inventory studies Type de document : Article/Communication Auteurs : Shi Pu, Auteur ; Martin Rutzinger, Auteur ; M. George Vosselman, Auteur ; Sander J. Oude Elberink, Auteur Année de publication : 2011 Article en page(s) : pp 28 - 39 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification ascendante hiérarchique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] reconnaissance de formes [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] zone urbaine dense Résumé : (Auteur) Road safety inspection is currently carried out by time-consuming visual inspection. The latest mobile mapping systems provide an efficient technique for acquiring very dense point clouds along road corridors, so that automated procedures for recognizing and extracting structures can be developed. This paper presents a framework for structure recognition from mobile laser scanned point clouds. It starts with an initial rough classification into three larger categories: ground surface, objects on ground, and objects off ground. Based on a collection of characteristics of point cloud segments like size, shape, orientation and topological relationships, the objects on ground are assigned to more detailed classes such as traffic signs, trees, building walls and barriers. Two mobile laser scanning data sets acquired by different systems are tested with the recognition methods. Performance analyses of the test results are provided to demonstrate the applicability and limits of the methods. While poles are recognized for up to 86%, classification into further categories requires further work and integration with imagery. Numéro de notice : A2011-516 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2011.08.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2011.08.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31410 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2011071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Knowledge based building facade reconstruction from laser point clouds and images / Shi Pu (2010)  

Public Titre : Knowledge based building facade reconstruction from laser point clouds and images Type de document : Thèse/HDR Auteurs : Shi Pu, Auteur Editeur : Delft : Netherlands Geodetic Commission NGC Année de publication : 2010 Collection : Netherlands Geodetic Commission Publications on Geodesy, ISSN 0165-1706 num. 75 Importance : 119 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-90-6132-319-3 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] B-Spline [Termes IGN] base de connaissances [Termes IGN] données laser [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] façade [Termes IGN] Pays-Bas [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] système à base de connaissances [Termes IGN] télémétrie laser terrestre [Termes IGN] texturage Index. décimale : 33.80 Lasergrammétrie Résumé : (Auteur) Various applications demand realistic 3D city models. For urban planning, analyzing in a 3D virtual reality world is much more efficient than imaging the 2D information on maps. For public security, accurate 3D building models are indispensable to make strategies during emergency situations. Navigation systems and virtual tourism also benefit from realistic city models. Manual creation of city models is undoubtedly a rather time consuming and expensive procedure. On one hand, images are for long the only data source for geometric modelling, while recovering of 3D geometries is not straightforward from 2D images. On the other hand, there are enormous amounts of objects (for example buildings) to be reconstructed, and their structures and shapes show a great variety. There is a lack of automated approaches to understand the building structures captured by data. The rapid development of cities even adds to the cost of manual city model updating. In recent years, laser scanning has been proven a successful technology for reverse engineering. The terrestrial laser point clouds are especially useful for documenting building facades. With the considerable high point density and the explicit 3D coordinates of terrestrial laser point clouds, it is possible to recover both large structures and fine details on building facades. The latest developments of mobile laser scanning technology also make it more cost-effective to take large-scale laser scanning over urban areas. This PhD research aims at reconstructing photorealistic building facade models from terrestrial laser point clouds and close range images, with a largely automatic process. A knowledge base about building facade structures is established first, where several important building features (wall, door, protrusion, etc.) are defined and described with their geometric properties and spatial relationships. Then constraints for feature extraction are derived from the knowledge base. After a laser point cloud is segmented into planar segments by surface a growing segmentation algorithm, each segment is compared with the feature constraints to determine the most likely feature type for each segment. The feature extraction method works fine for all facade features except for windows, because there are usually insufficient laser points reflected from window glass. Instead, windows are reconstructed from the holes on the wall features. Then outline polygons or B-spline surfaces are fit to all feature segments, and the parts without laser points are hypothesized according to knowledge. A complete polyhedron model is combined from both fitted and hypothesized outlines. Since laser data contains no colour information, the building models reconstructed from only laser data contain only geometric information such as vertices and edges. To obtain photorealistic results, textures must be mapped from images to the geometric models. The fusing of laser points and image requires accurate alignment between laser space and image space, which is accomplished after a semi-automated process. Because of the limitations of modelling methods, the geometry model reconstructed from laser points may contain many errors which would cause poor texturing effect. Therefore, significant line features extracted from images are compared with the initial model's edges, and necessary refinements are made to correct the model errors, or at least make the model edges consistent with the image lines. Finally, in the texturing stage, the texture of each model face is selected automatically from multiple images to ensure the optimal visibility. Texture errors caused by occlusions in front of a wall are also removed by analyzing the locations of the wall, the occlusions and the camera position. Experiments with three data sets show that building reconstruction are considerably accelerated by the presented methods. Our approach is more than 10 times faster than the traditional approach when reconstructing the same buildings, and the models by our approach contain more fine details such as doors and windows. The reconstruction of wall facades and roofs are fully automatic, while some manual interactions (48 percent of the total reconstruction time) are still required for editing the fine details. It should also be faster to make global statistics (number of floors, number of entrances, etc.) and modifications (deriving models with a lower level of detail, applying pre-defined textures, etc.) later on to our models, since different model parts have been associated with the semantic labels. While the reconstruction efficiency is improved by our approach, the visualization effects of our models are also comparable to the models by the traditional approach. The future work will focus on improving the knowledge base and developing a fully automated camera parameter estimation procedure. The completeness and adaptability of the knowledge base will be especially important for the further automation of our reconstruction approach. Note de contenu : 1 Introduction 1.1 State-of-the-art of terrestrial laser scanning 1.2 Related works 1.2.1 Overview 1.2.2 Frueh et al. 2005 1.2.3 Cornelis et al. 2008 1.2.4 Ripperda2008 1.2.5 Becker 2009 1.3 Method overview 1.4 Structure of the thesis 2 Knowledge engineering and reasoning 2.1 Knowledge engineering 2.1.1 Assembling the knowledge 2.1.2 Decide on a vocabulary 2.1.3 Encode general knowledge 2.1.4 The hierarchical composition 2.2 Reasoning with the knowledge 2.3 Managing uncertainty 2.3.1 Describing the uncertainty 2.3.2 Making expected decisions 2.4 Concluding remarks 3 Feature extraction 3.1 Preprocessing 3.1.1 Spatial indexing 3.1.2 Extracting points of interest 3.2 Extraction of geometric features 3.2.1 Flat surfaces 3.2.2 Curved surfaces 3.3 Extraction of semantic features 3.3.1 Solid features extraction 3.3.2 Hole-based window extraction 3.4 Discussion 4 Geometric reconstruction 4.1 Polygon fitting 4.1.1 Least squares fitting 4.1.2 Convex polygon and concave polygon fitting 4.1.3 Minimum bounding rectangle fitting 4.2 B-spline surface fitting 4.2.1 The B-spline curve and surface 4.2.2 B-spline surface approximation 4.3 Hypotheses for parts without laser data 4.4 Results and Discussion 4.4.1 Flat surfaces 4.4.2 Curved surfaces 5 Model refinement with imagery 5.1 Method overview 5.2 Registration 5.2.1 Perspective Conversion 5.2.2 Spatial Resection 5.2.3 Relative Orientation 5.3 The model refinement 5.3.1 Extraction of Significant Lines from Images 5.3.2 Matching Model Edges with Image Lines 5.3.3 Refinement Strategy 5.4 Test cases 5.4.1 The restaurant house 5.4.2 The town hall 5.4.3 The wall with high windows 5.4.4 Summary 5.5 Conclusions and outlook 6 Texture mapping 6.1 Selecting texture images 6.1.1 Optimal image selection 6.1.2 Occlusion removal 6.2 Calculating texture coordinates 6.3 Results and discussion 6.3.1 The three joined houses 6.3.2 The house with a balcony 6.3.3 The curved walls 6.3.4 Discussion 7 Method evaluation 7.1 The reconstruction approaches 7.1.1 Our approach 7.1.2 The traditional approach 7.2 The Vlaardingen case 7.3 The Enschede case 7.4 The Esslingen case 7.5 Conclusions 8 Conclusions and recommendations 8.1 Conclusions 8.2 Recommendations Numéro de notice : 10834 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : En ligne : https://www.ncgeo.nl/index.php/en/publicatiesgb/publications-on-geodesy/item/257 [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=62511

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
10834-01	33.80	Livre	Centre de documentation	Photogrammétrie - Lasergrammétrie	Disponible