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Introducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8600 - 8617 Titre : Introducing spatial regularization in SAR tomography reconstruction Type de document : Article/Communication Auteurs : Clément Rambour, Auteur ; Loïc Denis, Auteur ; Florence Tupin, Auteur ; Hélène Oriot, Auteur Année de publication : 2019 Article en page(s) : pp 8600 - 8617 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] acquisition comprimée [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] écho radar [Termes descripteurs IGN] fractionnement [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] mécanique de Lagrange [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] TerraSAR-X [Termes descripteurs IGN] tomographie radar Résumé : (auteur) The resolution achieved by current synthetic aperture radar (SAR) sensors provides a detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building facade, and roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: 1) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing) and 2) transformation from radar geometry to ground geometry and extraction of significant scatterers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale nonconvex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate, on simulated data and a TerraSAR-X tomographic data set, the potential of this approach to produce 3-D reconstructions of urban surfaces. Numéro de notice : A2019-596 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921756 date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94588 [article]

RoofN3D: a database for 3D building reconstruction with deep learning / Andreas Wichmann in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 435 - 443 Titre : RoofN3D: a database for 3D building reconstruction with deep learning Type de document : Article/Communication Auteurs : Andreas Wichmann, Auteur ; Amgad Agoub, Auteur ; Valentina Schmidt, Auteur Année de publication : 2019 Article en page(s) : pp 435 - 443 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] .Net [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données localisées 3D [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] toit Résumé : (Auteur) Machine learning methods, in particular those based on deep learning, have gained in importance through the latest development of artificial intelligence and computer hardware. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a three-dimensional (3D) point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. The details about RoofN3D and the developed framework to automatically derive such training data are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to 3D point cloud data are presented. Finally, we exemplarily demonstrate how the provided data can be used to classify building roofs with the PointNet framework. Numéro de notice : A2019-248 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.435 En ligne : https://doi.org/10.14358/PERS.85.6.435 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93004 [article]

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Automatic reconstruction of fully volumetric 3D building models from oriented point clouds / Sebastian Ochmann in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 251 - 262 Titre : Automatic reconstruction of fully volumetric 3D building models from oriented point clouds Type de document : Article/Communication Auteurs : Sebastian Ochmann, Auteur ; Richard Vock, Auteur ; Reinhard Klein, Auteur Année de publication : 2019 Article en page(s) : pp 251 - 262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Industry foudation classes IFC [Termes descripteurs IGN] modélisation 3D du bâti BIM [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] programmation linéaire [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] semis de points Résumé : (Auteur) We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds. Numéro de notice : A2019-210 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.017 date de publication en ligne : 30/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92676 [article]

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Geometric comparison and quality evaluation of 3D models of indoor environments / H. Tran in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 29 - 39 Titre : Geometric comparison and quality evaluation of 3D models of indoor environments Type de document : Article/Communication Auteurs : H. Tran, Auteur ; Kourosh Khoshelham, Auteur ; Allison Kealy, Auteur Année de publication : 2019 Article en page(s) : pp 29 - 39 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] erreur géométrique [Termes descripteurs IGN] espace intérieur [Termes descripteurs IGN] évaluation des données [Termes descripteurs IGN] modèle 3D du site [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] test de performance Résumé : (Auteur) The increasing demand for automated, cost-effective and time-efficient indoor modelling methods leads to a need for performance evaluation of these methods by assessing the quality of the reconstructed models. In this paper, we introduce a method for geometric comparison of a 3D indoor model with a reference, which is useful not only for evaluating the geometric quality of the model, but also for change detection and temporal analysis of the building. The method provides suitable criteria for the quantitative evaluation of the geometric quality in terms of completeness, correctness, and accuracy. Experimental evaluation on a synthetic dataset and the ISPRS benchmark dataset shows the potential of the proposed method for quantitative evaluation and localization of geometric errors in 3D models of indoor environments. Numéro de notice : A2019-126 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.012 date de publication en ligne : 19/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92436 [article]

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Repeated structure detection for 3D reconstruction of building façade from mobile lidar data / Yanming Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 2 (February 2019) . - pp 93 - 108 Titre : Repeated structure detection for 3D reconstruction of building façade from mobile lidar data Type de document : Article/Communication Auteurs : Yanming Chen, Auteur ; Xiaoqiang Liu, Auteur ; Mengru Yao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 93 - 108 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] façade [Termes descripteurs IGN] reconstruction 3D du bâti Résumé : (Auteur) This study proposes a new method for repeated structure detection and the three-dimensional (3D) reconstruction of building façades from mobile lidar data. Firstly, the building façade is divided and unrolled to simplify the complex façade structures, improving the automation of structure detection and building reconstruction. Subsequently, the unrolled façade is decomposed into tiles by analyzing the repeated structures. Tiles with strong similarities are matched and merged to restore the imperfect façade points. Based on the restored points and repeated structures, a 3D building façade can be reconstructed with a complete structure and fine detail. An analysis is conducted to compare the constructed 3D model with the lidar points of actual façade. The results of this analysis demonstrate that the proposed method can effectively deal with missing areas caused by occlusion, viewpoint limitation, and uneven point density, as well as realizing the highly complete 3D reconstruction of a building façade. Numéro de notice : A2019-059 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.2.93 date de publication en ligne : 01/02/2019 En ligne : https://doi.org/10.14358/PERS.85.2.93 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92108 [article]

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Digital preservation, social history, and the Quon Sang Lung Laundry building : a case study from Fort Macleod, Alberta, Canada / Peter Dawson in Applied geomatics, vol 10 n° 4 (December 2018)  

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Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds / Xiang Gao in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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Three-dimensional building façade segmentation and opening area detection from point clouds / S.M. Iman Zolanvari in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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On a novel 360° panoramic stereo mobile mapping system / Stefan Blaser in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)  

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Qualification sémantique de modèles 3D de bâtiments / Oussama Ennafii (2018)    

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Documentation of heritage buildings using close-range UAV images: dense matching issues, comparison and case studies / Arnadi Murtiyoso in Photogrammetric record, vol 32 n° 159 (September 2017)  

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Urban building reconstruction from raw LiDAR point data / Cheng Yi in Computer-Aided Design, vol 9x (2017)  

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3D building roof reconstruction from airborne LiDAR point clouds : a framework based on a spatial database / Rujun Cao in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

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BIM and all that jazz / Stuart Cadge in GEO: Geoconnexion international, vol 16 n° 2 (February 2017)

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Statistical Relational Learning of Grammar Rules for 3D Building Reconstruction / Youness Dehbi in Transactions in GIS, vol 21 n° 1 (February 2017)  

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