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Geometric and semantic joint approach for the reconstruction of digital models of buildings / Pierre-Alain Langlois (2021)
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Titre : Geometric and semantic joint approach for the reconstruction of digital models of buildings Type de document : Thèse/HDR Auteurs : Pierre-Alain Langlois, Auteur ; Renaud Marlet, Directeur de thèse ; Alexandre Boulch, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2021 Importance : 131 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat de l’Ecole des Ponts ParisTech, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] jeu de données localisées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] reconnaissance de surface
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] texture d'imageIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The advent of Building Information Models (BIM) in the field of construction and city management revolutionizes the way we design, build, operate and maintain our buildings. BIM models not only include the geometric aspect of the buildings but also semantic information which identifies its logical components (walls, slabs, windows, doors, etc..). While this information is fairly reasonable to create during the building design, only 1% of the building stock is renewed each year. There is therefore an increasing need for automated methods to generate BIM models on existing buildings from sensors such as simple RGB cameras or more advanced Lidar sensors which directly provide a point cloud.In this context, the goal of this thesis is to develop approaches for BIM reconstruction, including both geometric reconstruction and semantic analysis.These tasks have been explored, but an important research effort is conducted to make them more robust to the variety of use cases found in practice.3D reconstruction is usually operated based on direct 3D acquisitions such as Lidars or using photogrammetry, i.e., using pictures to triangulate key point locations and reconstruct the surface afterward. In the context of buildings, the later case is usually limited by the presence of textureless areas which make it hard for the algorithms to find key points and to triangulate them. Moreover, some parts of the buildings might be missing from the input data because of occlusions or omission from the acquisition operator.Regarding semantics in point clouds, important ambiguities exist between semantic classes: the discontinuity between a wall and a door can be hard to distinguish; a slab, a roof and a ceiling sometimes need additional context to be disentangled.In this thesis, we present three technical contributions to address these issues.First, for 3D reconstruction of building scenes, we propose the first method to reconstruct piecewise-planar scenes from images using line segments as primitives. While wide textureless areas exist in indoor scenes (e.g., walls), making it particularly difficult to detect key points, lines are often more visible and easier to detect (e.g., change of illumination at the intersection of two walls) and therefore should be used to ensure robustness of image-based reconstruction approaches. We leverage the presence of these line segments and the possibility to detect and triangulate them. This makes the method robust to textureless surfaces, and we show that we can reconstruct scenes on which point-based methods fail.The second contribution is more theoretical and addresses the problem of mesh reconstruction from multiple calibrated images of low resolution. In this context, traditional methods completely fail and directly learning priors on a large scale dataset of 3D shapes allows us to still perform reconstruction. More specifically, our method uses the learned priors to provide an initial rough shape which is further refined by incorporating geometric constraints. Our method directly outputs a mesh and competes with state of the art methods which can only output a noisy point cloud.Finally, the third technical contribution is VASAD, a dataset for volumetric and semantic reconstruction, which we created from raw BIM models available online. It is the first large scale dataset (62000m²) to offer both geometric and semantic annotation at point and mesh level. With this dataset, we propose two methods to jointly reconstruct both geometry and semantics from a point cloud and we show that the proposed dataset is challenging enough to stimulate research. Note de contenu : 1. Introduction
1.1 Motivation
1.2 Approach
1.3 Contributions
1.4 Organization of the dissertation
SURFACE RECONSTRUCTION FROM 3D LINE SEGMENTS
2. Introduction
2.1 Reconstructing textureless surfaces
2.2 Related Work
3. Method
3.1 Line extraction
3.2 Plane detection from 3D line segments
3.3 Surface reconstruction
4. Results
4.1 Datasets
4.2 Observations on the input data
4.3 Qualitative evaluation of reconstructions
4.4 Quantitative evaluation of reconstructions
4.5 Ablation study
4.6 Limitations and perspectives
4.7 Conclusion
3D RECONSTRUCTION BY PARAMETERIZED SURFACE MAPPING
5. Introduction
5.1 Learning 3D reconstruction
5.2 Related work
6. Method
6.1 Learning a Multi-View Parameterized Surface Mapping
6.2 Design choices
7. Results
7.1 Dataset
7.2 Evaluation criteria
7.3 Experimental results
7.4 Ablation study
7.5 Discussion and limitations
7.6 Conclusion
VASAD: A VOLUME AND SEMANTIC DATASET FOR BUILDING RECONSTRUCTION FROM POINT CLOUDS
8. Introduction
8.1 3D Reconstruction for buildings
8.2 Related work
9. DATASET
9.1 Building information models
9.2 Presentation of the dataset
9.3 3D representation
9.4 Point cloud simulation
9.5 Train/test split
10. Method
10.1 Reconstruction approaches
10.2 PVSRNet
10.3 Semantic Convolutional Occupancy Network
10.4 Data preparation
11. RESULTS
11.1 Metrics
11.2 Surface reconstruction
11.3 Semantic segmentation
11.4 Discussion
11.5 Conclusion
EPILOGUE
12. Conclusion
12.1 Looking back
12.2 Looking aheadNuméro de notice : 26822 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : informatique : Champs-Sur-Marne : 2021 Organisme de stage : Laboratoire d'Informatique Gaspard Monge LIGM nature-HAL : Thèse DOI : sans Date de publication en ligne : 11/04/2022 En ligne : https://tel.hal.science/tel-03637158/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100564
Titre : Polarimetric Synthetic Aperture Radar : principles and application Type de document : Monographie Auteurs : Irena Hajnsek, Auteur ; Yves-Louis Desnos, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2021 Collection : Remote sensing and digital image processing num. 25 Importance : 294 p. ISBN/ISSN/EAN : 978-3-030-56504-6 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] agriculture
[Termes IGN] bande X
[Termes IGN] cryosphère
[Termes IGN] détection d'arbres
[Termes IGN] détection du bâti
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Radarsat
[Termes IGN] image Terra
[Termes IGN] occupation du sol
[Termes IGN] polarimétrie radar
[Termes IGN] radar à antenne synthétique
[Termes IGN] zone humideIndex. décimale : 35.22 Télédétection en hyperfréquence - Traitement d'image radar Résumé : (Editeur) This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans. Note de contenu : - Basic Principles of SAR Polarimetry
- Forest Applications
- Agriculture and Wetland Applications
- Cryosphere Applications
- Urban Applications
- Ocean ApplicationsNuméro de notice : 26542 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-030-56504-6 En ligne : http://doi.org/10.1007/978-3-030-56504-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97766 Relation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds / Yuan Li in Remote sensing, vol 13 n° 1 (January-1 2021)
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[article]
Titre : Relation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Wu Bo, Auteur Année de publication : 2021 Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] CityGML
[Termes IGN] contrainte géométrique
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] géomètrie algorithmique
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] relation topologique
[Termes IGN] semis de points
[Termes IGN] ville intelligenteRésumé : (auteur) The complexity and variety of buildings and the defects of point cloud data are the main challenges faced by 3D urban reconstruction from point clouds, especially in metropolitan areas. In this paper, we developed a method that embeds multiple relations into a procedural modelling process for the automatic 3D reconstruction of buildings from photogrammetric point clouds. First, a hybrid tree of constructive solid geometry and boundary representation (CSG-BRep) was built to decompose the building bounding space into multiple polyhedral cells based on geometric-relation constraints. The cells that approximate the shapes of buildings were then selected based on topological-relation constraints and geometric building models were generated using a reconstructing CSG-BRep tree. Finally, different parts of buildings were retrieved from the CSG-BRep trees, and specific surface types were recognized to convert the building models into the City Geography Markup Language (CityGML) format. The point clouds of 105 buildings in a metropolitan area in Hong Kong were used to evaluate the performance of the proposed method. Compared with two existing methods, the proposed method performed the best in terms of robustness, regularity, and topological correctness. The CityGML building models enriched with semantic information were also compared with the manually digitized ground truth, and the high level of consistency between the results suggested that the produced models will be useful in smart city applications. Numéro de notice : A2021-078 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010129 Date de publication en ligne : 01/01/2021 En ligne : https://doi.org/10.3390/rs13010129 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96820
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 13[article]Steps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery / Debasish Chakraborty in Geocarto international, vol 36 n° 1 ([01/01/2021])
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[article]
Titre : Steps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery Type de document : Article/Communication Auteurs : Debasish Chakraborty, Auteur ; Pranshu Kumar, Auteur Année de publication : 2021 Article en page(s) : pp 110 - 120 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arborescence
[Termes IGN] comptage
[Termes IGN] détection de contours
[Termes IGN] houppier
[Termes IGN] image à haute résolution
[Termes IGN] image à très haute résolution
[Termes IGN] image satellite
[Termes IGN] image Worldview
[Termes IGN] itération
[Termes IGN] segmentation d'imageRésumé : (Auteur) In this study primarily, high-resolution (HR) satellite image is segmented into tree and non-tree regions. Thereafter plots the local minima in the segmented image. Point surrounded by the higher intensity values is called as local minima. The local minimum is the starting point for marking the tree crown boundary. The adjacent darker points along the local minima are plotted in a specific direction for marking the tree crown boundary. Subsequently a seven steps iterative procedure is followed for delineating and counting the tree crowns. The validation of the method is done on WorldView-2 data. Numéro de notice : A2021-054 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1611947 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1611947 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96779
in Geocarto international > vol 36 n° 1 [01/01/2021] . - pp 110 - 120[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2021011 RAB Revue Centre de documentation En réserve L003 Disponible Study of an integrated pre-processing architecture for smart-imaging-systems, in the context of lowpower computer vision and embedded object detection / Luis Cubero Montealegre (2021)
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Titre : Study of an integrated pre-processing architecture for smart-imaging-systems, in the context of lowpower computer vision and embedded object detection Type de document : Thèse/HDR Auteurs : Luis Cubero Montealegre, Auteur Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 161 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, spécialité : Nano-Electronique et Nano-TechnologiesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approche hiérarchique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détecteur CMOS
[Termes IGN] détection d'objet
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] instrument embarqué
[Termes IGN] intelligence artificielle
[Termes IGN] restauration d'imageIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Embedded Computer vision, as many real application scenarios other areas of artificial intelligence, is facing hardware and power constraints with the rising of edge computing applications. For instance, the object detection problem, consisting in finding different objects of specific classes (types) in an image, turns out to be quite complicated to embed near the image sensor as two complex tasks are required: multi-scale localization and multi-class classification (i.e. identifying bounding boxes that perfectly enclose each object, whatever its size, and labeling the type of the detected object). Today these tasks are mainly often performed on general-purpose desktop machines. Nevertheless, attractive applications like autonomous-driving, augmented reality or video surveillance are urging the need for low-power, low-latency and compact low power devices.The state of the art has approached this challenge by optimizing specific sections of the complete processing-pipeline for a comparable object detection performance. A typical example in the last decade corresponds to minimizing the computing precision, hence the power, to a minimal value. Diminishing the bit-depth or image size has then been studied while implementing pre-processing steps that increase robustness against the loss in bit and image resolution. An algorithm that doesn’t require that kind of pre-processing stage to be programmable is obviously desirable in order to simplify its implementation (e.g. no memory access to learned weights). Another strategy has been to reduce power due to I/O communications amongst different chips or devices thanks to a more exhaustive integration of specialized circuitry and thanks to more efficient memory accesses and mathematical operations.In that context of near-sensor computing, this work points towards a more energy efficient detection pipeline. We target several specific key aspects:1. We try to assess if a dedicated-class-agnostic region proposal algorithm, based on pre-processed low-level features, could replace the typical sliding window approach for object localization in integrated smart imaging systems, allowing to target more efficiently objects in the image. Then, we propose a pipeline that takes into account near image sensor features extraction for Region Proposals with an embedded version of an algorithm called EdgeBoxes.2. We try to assess an optimal type of pre-processing (based on an efficient architecture) that would allow extracting low level features (oriented gradients), and give the best trade-off between power consumption, hardware complexity and object detection performance. Specifically, while being this architecture is fully compatible with region proposal algorithms beyond the sliding window.3. Finally, we try to assess if non-standard, or neuromorphic, image acquisition techniques can be exploited in order to further increase the detection efficiency in real case scenarios.Our methodology relies on behavioral simulations carried out thanks to a custom framework written in Python and C++ code. We propose a hierarchical model (and code architecture) of different image acquisition and processing techniques, and we study their performance through specific metrics related to runtime, memory usage, hardware complexity, I/O data-rate, localization performance and classification performance. We provide comparison with the state of the art and several benchmarks giving guidance to choose one or another architecture depending on the specific needs, and we conclude by stating which one would give, from our perspective, the best trade-offs. Note de contenu : 1. Introduction
2. State of the art
3. Our simulation Framework
4. Region proposals pipeline design
5. Embedded Edge Extraction Circuitry
6. Object Localization benchmarks
7. Dynamic Vision Pre-processing
8. ConclusionNuméro de notice : 28692 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Nano-Electronique et Nano-Technologies : Grenoble : 2021 Organisme de stage : LETI DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03612476/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100289 Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)
PermalinkBuilding change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)
PermalinkTopographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])
PermalinkTextural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])
PermalinkA graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)
PermalinkCrater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkPrecise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkSemi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkShoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method / Audrey Minghelli in Remote sensing, vol 12 n° 16 (August-2 2020)
PermalinkSemi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)
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