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Titre : Acquisition of 3D topography : automated 3D road and building reconstruction using airborne laser scanner data and topographic maps Type de document : Thèse/HDR Auteurs : Sander J. Oude Elberink, Auteur Editeur : Delft : Netherlands Geodetic Commission NGC Année de publication : 2010 Collection : Netherlands Geodetic Commission Publications on Geodesy, ISSN 0165-1706 num. 74 Importance : 172 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-90-6132-318-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bati
[Termes IGN] carte topographique
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] lasergrammétrie
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] réseau routier
[Termes IGN] télémétrie laser aéroportéIndex. décimale : 33.80 Lasergrammétrie Résumé : (Auteur) Introduction and research goal : Our research covers the automation in acquiring three dimensional (3D) topographic objects. The research tasks focus on two specific objects: roads and buildings. These objects are of high importance in 3D city models as they are two major topographic classes in the urban environment. Our activities are located between: -1. how topographic objects exist in reality; -2. how they are captured in the data, and -3. how they appear in a modelled/virtual world. To accomplish an automated approach, existing 2D topographic maps are upgraded to 3D using airborne laser scanner data. 3D topography also includes multiple heights or even multiple objects on top of each other at a certain location. The essence in the research activities on roads differs basically from those on buildings. For roads the focus is on reconstructing the edges' height of the objects, whereas for buildings the challenge is to reconstruct the 3D polyhedral roof shape inside the building edges.
3D Road reconstruction : When examining 3D road objects, we can expect that multiple road objects cross at a certain location. An automated method for 3D modelling of complex highway interchanges is presented. Laser data and 2D topographic map data are combined in an innovative 3D reconstruction procedure. Complex situations demand for knowledge to guide the automatic reconstruction. This knowledge is used in the fusion procedure to constrain the topological and geometrical properties of the reconstructed 3D model. Laser data has been segmented and filtered before it is fused with map data. In the surface-growing algorithm combining map and laser points, the laser data is assigned to the corresponding road element. Elevations of map points are determined by least squares plane fitting through a selection of neighbouring laser points. Although results are shown using two specific data sources, the algorithm is designed to be capable of dealing with any polygon-based topographic map and any aerial laser scanner data set. Quality analysis is essential for developing a reliable reconstruction process and for a proper use of 3D data. The quality of 3D reconstructed roads strongly depends on accuracy and type of input data and the reconstruction processing steps. We predict the precision of reconstructed map elevations by propagating errors in the input data through the processing steps. Besides this quality prediction, we test the reconstructed model against independent reference data. Differences between these two datasets are explained by the predicted uncertainty in the model. Map point heights can be reconstructed with an average precision of 10 to 15 cm, depending on the laser point configuration.
3D. Building reconstruction : The building reconstruction task contains three main goals: -1. to select laser points belonging to building roofs, -2. to detect the roof structure of that building, and -3. to reconstruct the outlines of the roof. We present a building reconstruction approach, which is based on a target graph matching algorithm as intermediate step to relate laser data with building models. Establishing this relation is important for adding building knowledge to the data. Our targets are topological representations of the most common roof structures which are stored in a database. Laser data is segmented into planar patches. The segments that are selected in the segment-in-polygon algorithm are considered initial roof segments. Topological relations between segments, in terms of intersection lines and height jumps, are represented in a building roof graph. These relations are labelled according to their geometry and that of the segments (e.g. same/opposite normal direction, convex/concave, tilted/horizontal). This graph is matched with the graphs from the target database. Matching results describe which target objects appear topologically in the data. Our target based graph matching algorithm supports the first two goals. The matching algorithm performs a filtering task: data features that topologically correspond with common roof structures are considered to be part of the roof structure of that building. These data features will be transferred to our automated building reconstruction, where the outlines of the roof faces have to be reconstructed. Segments and intersection lines that do not fit to an existing target roof topology will be removed from the further automated reconstruction approach. The reconstruction algorithm covers the third main goal of our building reconstruction task. For the geometric reconstruction, we present two approaches that vary in the amount of information they take from the data. The first, more data driven approach starts with laser data features that have been matched with target models. In general, the matched intersection lines represent the interior of the roof structure, so the task is to find an appropriate solution for the remaining roof edges, e.g. eaves and gutters. Map data is used for selection of roof segments and is taken as location for walls. Therefore we need to split up map polygons in order to build walls that distinguish various height levels, e.g. at step edge locations. The second, more model driven approach reconstructs parameterised building models. This approach relies more on geometric assumptions, such as roof symmetry, but the models can be refined if the data deviates significantly from the model. The target information includes the details on how these deviations are determined and on the thresholds to decide what is significant or not. We present results of 3D reconstructed models, including several quality checks. These quality measures describe the completeness of the match results plus the correctness of assumptions to the roof outline. About 20% of the buildings are affected by segments that did not completely match with the target graphs. In a few of these cases, this is correct because the segment is not representing a roof face. However, in about 40% of these cases, a neighbouring segment that would complete a target match is missing. Adapting processing parameters, such as minimum segment size, may improve the result but it may also disturb other topological relations. Setting the parameters is therefore an important task for the operator. Specially, parameters that define the segmentation algorithm are crucial as the segment is the key data feature in our building reconstruction algorithm. In order to improve our matching algorithm, the likelihood of relations between segments could be included in the attribute list of edges in the roof topology graph. At the moment only information on the geometric appearance of the intersection line is given as attribute value to the corresponding graph edge. Future work includes defining likelihood functions for graph edges and analysing the effect of likelihood attributes.Note de contenu : Part 1: Introduction to acquisition of 3D topography
1 Introduction
1.1 3D Topography
1.2 Scope and limitations
1.3 Input data
1.4 Research problems
1.5 Goal and objectives
1.6 Importance
1.7 Thesis outline
2 Use of 3D topography
2.1 Introduction
2.2 User requirements
2.2.1 Municipality of Den Bosch
2.2.2 Survey Department of Rijkswaterstaat
2.2.3 Water board "Hoogheemraadschap de Stichtsche Rijnlanden"
2.2.4 Topographic Service of the Dutch Cadastre
2.3 Re-using 3D models
2.3.1 Municipality of Den Bosch
2.3.2 Survey Department of Rijkswaterstaat
2.3.3 Water board "Hoogheemraadschap de Stichtsche Rijnlanden"
2.3.4 Topographic Service of the Dutch Cadastre
2.3.5 Availability and distribution
2.3.6 Data fusion
2.3.7 Generalization and filtering
2.3.8 3D Represents as-is situation
2.4 Role of use cases in research project
2.5 Recent developments in using 3D topography
2.6 Conclusions
Part 2: 3D Roads
3 3D Reconstruction of roads
3.1 Introduction
3.2 Related work
3.2.1 Road reconstruction from aerial images
3.2.2 2D Road mapping from laser data
3.2.3 3D Reconstruction from laser data
3.3 Proposed approach
3.4 Data sources
3.4.1 Airborne laser scanner data
3.4.2 Pre-processing laser data
3.4.3 2D Topographic map data
3.4.4 Pre-processing 2D map
3.5 Fusion of map and laser data
3.5.1 Research problems on fusing map and laser data
3.5.2 Proposed fusion algorithm
3.6 3D Reconstruction of polygons
3.6.1 Polygon boundaries
3.6.2 Additional polygons
3.6.3 Assumptions on boundaries
3.6.4 Surfaces
3.7 Results
3.7.1 Interchange "Prins Clausplein"
3.7.2 Interchange "Waterberg"
3.8 Discussion
3.8.1 Parameter settings
3.8.2 Topological correctness
4 Quality analysis on 3D roads
4.1 Error propagation
4.1.1 Quality of plane at map point location
4.1.2 Quality of laser block
4.1.3 Quality of plane model
4.2 Reference data
4.2.1 Height differences between reference data and 3D model
4.3 Testing of predicted quality
4.4 Discussion
Part 3: 3D Buildings
5 Building shape detection
5.1 Introduction
5.1.1 Real buildings vs 3D model representation
5.1.2 Real buildings vs appearance in input data
5.1.3 Appearance in input data vs 3D model representation
5.2 Related work
5.2.1 2D Mapping of building outlines
5.2.2 3D Reconstruction of buildings
5.3 Research problems
5.3.1 Problems on roof shape detection
5.3.2 Problems on scene complexity
5.4 Proposed approach
5.5 Information from map data
5.6 Features from laser data
5.6.1 Segmentation of laser scanner data
5.6.2 Intersection lines
5.6.3 Step edges
5.6.4 Roof topology graph
5.7 Target graphs
5.8 Target based graph matching
5.9 Complete matching results
5.10 Incomplete matching results
6 3D Building Reconstruction
6.1 Introduction
6.2 Components of a roof boundary
6.3 Approach 1: Combine features from complete match results
6.4 Extension of horizontal intersection lines
6.5 Outer boundaries of roof faces
6.5.1 Flat roof faces
6.5.2 Eave construction
6.5.3 Gutter construction
6.6 Dormers and step edges
6.6.1 Simple dormers
6.6.2 Step edges
6.6.3 Step edges for map subdivision
6.7 Reconstruction of walls
6.8 Approach 2: reconstructed targets
6.8.1 Parameterised target models
6.8.2 Use of map data
6.8.3 Limitations
6.8.4 Potential use
6.9 Summary
7 Results and evaluation
7.1 Introduction
7.2 Results
7.2.1 Approach 1: Combined features
7.2.2 Approach 2: Reconstructed targets
7.3 Evaluation
7.3.1 Laser data features
7.3.2 Evaluation on target based matching
7.3.3 Reconstructed models
7.3.4 Problematic situations
7.3.5 Performance in time
7.4 Potential for nation wide 3D building database
7.5 Summary
Part 4: Conclusions and recommendations
8 Conclusions and recommendations
8.1 Conclusions
8.1.1 3D Topographic object reconstruction
8.1.2 3D Road reconstruction
8.1.3 3D Building reconstruction
8.2 RecommendationsNuméro de notice : 10833 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère DOI : sans En ligne : https://www.ncgeo.nl/index.php/en/publicatiesgb/publications-on-geodesy/item/258 [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=62510 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 10833-01 33.80 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible Extraction of vertical posts in 3D laser point clouds acquired in dense urban areas by a mobile mapping system / Sterenn Liberge (2010)
contenu dans Photogrammetric computer vision and image analysis: ISPRS Commission 3 symposium, Saint-Mandé, 1-3 septembre 2010, volume 2. Papers accepted on the basis of abstracts / Nicolas Paparoditis (2010)
Titre : Extraction of vertical posts in 3D laser point clouds acquired in dense urban areas by a mobile mapping system Type de document : Article/Communication Auteurs : Sterenn Liberge, Auteur ; Bahman Soheilian , Auteur ; Nesrine Chehata , Auteur ; Nicolas Paparoditis , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2010 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 38-3B Conférence : PCV 2010, ISPRS - Commission 3 symposium Photogrammetric computer vision and image analysis 01/09/2010 03/09/2010 Saint-Mandé France ISPRS OA Archives Importance : pp 126 - 130 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] arbre de décision
[Termes IGN] extraction automatique
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) This paper focuses on extracting vertical objects from 3D terrestrial point clouds, acquired in dense urban scenes. We are especially interested in urban vertical posts whose inventory is useful for many applications such as urban mapping, virtual tourism or localization. The proposed methodology is based on two steps. The first is a focalization step providing hypothetical candidates consisting of vertical features. The second step validates or rejects these candidates. In the case of validation, the features are classified according to the posts pattern library. The extraction of vertical objects is processed by projecting the point cloud in a horizontal plan. The accumulation density, the minimal and maximal heights are used to filter out ground points, tree leaves and to solve acquisition problems such as a region multiscans. After filtering step, the accumulation image is updated. Connex regions correspond to the vertical objects. These candidates are then validated regarding the posts pattern library. An analysis of the 3D vertical point distribution is processed. To do that, each region is characterized by its eigenvalues and eigenvectors based on a Principal Component Analysis in 3D space. The classification is processed by a decision tree algorithm. Results are presented on large and various datasets acquired under real conditions in a dense urban area. A global accuracy of 84 % is reached. Numéro de notice : C2010-006 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://www.isprs.org/proceedings/XXXVIII/part3/b/pdf/126_XXXVIII-part3B.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=65061 Factors influencing pulse width of small footprint, full wave form airborne laser scanning data / Y.C. Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 1 (January 2010)
[article]
Titre : Factors influencing pulse width of small footprint, full wave form airborne laser scanning data Type de document : Article/Communication Auteurs : Y.C. Lin, Auteur ; Jon P. Mills, Auteur Année de publication : 2010 Article en page(s) : pp 49 - 59 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] balayage laser
[Termes IGN] classification automatique
[Termes IGN] impulsion laser
[Termes IGN] lidar à retour d'onde complète
[Termes IGN] longueur d'onde
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Small footprint, full waveform airborne laser scanning provides the opportunity to derive high-resolution geometric and physical information simultaneously from a single scanner system. This study evaluates the influence of various factors on the shape of the returned waveform and investigates the possibility of improving terrain classification by applying waveform-derived information. The factors discussed are surface roughness, slope angle, scan angle, amplitude, and footprint size. It is statistically demonstrated that roughness is the most significant factor affecting pulse width, and that, over relatively smooth surfaces, there is no significant variation in pulse width behavior resulting from different footprint sizes. Pulse width also exhibits a relatively stable behavior when amplitude, range distance, or scan angle vary substantially. The overall accuracy of classification achieved by applying pulse width information over all the different land-cover types examined in this study (including scrub, hillside, single trees, and forest areas) was greater than 85 percent, with > 94 percent achieved for open vegetation areas. Physical surface information provided by small footprint waveform data is considered to be at the microscale, therefore it is recommended to combine such information with geometry (e.g., filtering algorithms) for the optimal identification of terrain points. Copyright ASPRS Numéro de notice : A2010-013 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.1.49 En ligne : https://doi.org/10.14358/PERS.76.1.49 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30209
in Photogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 1 (January 2010) . - pp 49 - 59[article]In-flight quality assessment and data processing for airborne laser scanning / Philipp Schaer (2010)
Titre : In-flight quality assessment and data processing for airborne laser scanning Type de document : Thèse/HDR Auteurs : Philipp Schaer, Auteur ; Jan Skaloud, Directeur de thèse Editeur : Zurich : Schweizerischen Geodatischen Kommission / Commission Géodésique Suisse Année de publication : 2010 Collection : Geodätisch-Geophysikalische Arbeiten in der Schweiz, ISSN 0257-1722 num. 79 Importance : 166 p. Format : 20 x 30 cm ISBN/ISSN/EAN : 978-3-908440-23-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] estimation statistique
[Termes IGN] étalonnage d'instrument
[Termes IGN] étalonnage en vol
[Termes IGN] géoréférencement direct
[Termes IGN] implémentation (informatique)
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] qualité des données
[Termes IGN] semis de points
[Termes IGN] télémétrie laser aéroportéIndex. décimale : 33.80 Lasergrammétrie Résumé : (Auteur) [introduction] [...] The objectives of this research are threefold: 1. Elaborate the theoretical concepts and methodologies needed for performing fast and automated QA/QC of ALS data. This embraces the definition of a methodology to automatically assess the quality of laser measurements and to evaluate the point-cloud coverage and homogeneity. A further objective is to establish a concept to assess the accuracy of derived surface models. 2. Implement the theoretical concepts in a fully functional in-flight QA/QC tool embedded in an ALS system. This requires merging data streams from different technologies (i.e. inertial navigation, GNSS positioning, laser measurements) in real-time (RT), developing its qualitative evaluation and presenting it to the system operator. 3. Provide a thorough analysis of the system performance using data acquired under real operating conditions. The objective is to demonstrate the usefulness of the provided QC information in-flight and to determine the achievable accuracies for ALS data processed in-flight. A particular attention is payed to the evaluations of benefits using real-time Kinematics (RTK) for improving the accuracy of the RT navigation, point-cloud generation and derivation of quality metrics. [...] Note de contenu : 1 Introduction
1.1 Context
1.2 Research Objectives
1.3 Methodology
1.4 External Contributions
1.5 Thesis Outline
2 ALS Enabling Technologies
2.1 Airborne Laser Scanning
2.1.1 History of ALS Technology
2.1.2 Current in-flight QA/QC Capabilities
2.1.3 Trends in ALS
2.2 Direct Georeferencing: Basic Relations
2.3 Laser Scanner Technology
2.4 Positioning Technology
2.5 Integrated Navigation Technology
3 ALS System Calibration and Point-cloud Processing
3.1 System Calibration
3.2 Strip Adjustment
3.3 ALS Point-cloud Data Processing
3.4 Digital Elevation Models
4 Point-cloud Quality Assessment
4.1 Overview of ALS Error Sources
4.2 ALS Navigation Errors
4.3 ALS System Errors
4.4 Assessment of ALS Target Accuracy
4.5 Assessment of Scanning Geometry
4.6 Single Point Quality Indicator
4.7 Error Budget Evaluation
4.8 Use of Quality Indicators in Point-cloud Processing
5 Surface Quality Assessment
5.1 Data Coverage Analysis
5.2 Internal Data Accuracy
5.3 Height Model Data Accuracy .
6 Implementation
6.1 Handheld Airborne Mapping System
6.2 Flight Preparation
6.3 In-flight Quality Assessment Tool (IQUAL)
6.4 GPS Quality Analysis Module (GPSQUAL)
6.5 RT GPS/INS Integration Engine (GIINAV)
6.6 RT ALS Georeferencing Engine (LIEOS)
6.7 LiDAR Quality Analysis Module (LIAN)
6.8 Flight Management and Monitoring Module (HELIPOS)
7 Results and Performance Analysis
7.1 RT Trajectory and Point-cloud Accuracy
7.2 Trajectory Quality Analyses (GPSQUAL)
7.3 ALS Point-cloud Quality Analysis (LIAN)
7.4 Computational Performance
8 Conclusion and Perspectives
8.1 Summary of Contributions
8.2 Conclusions
8.3 Perspectives
Bibliography
A Derivation of Sub-matrices
B Computation of 3D Laser Footprint
C Comparison RT - PPNuméro de notice : 10368 Affiliation des auteurs : non IGN Autre URL associée : URL EPFL Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : : EPFL : 2010 DOI : 10.5075/epfl-thesis-4590 En ligne : https://www.sgc.ethz.ch/sgc-volumes/sgk-79.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=62407 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 10368-01 33.80 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible
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] texturageIndex. 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 RecommendationsNuméro 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 Réservation
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