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Close-range photogrammetry and 3D imaging / Thomas Luhmann (2020)

Public Titre : Close-range photogrammetry and 3D imaging Type de document : Monographie Auteurs : Thomas Luhmann, Auteur ; Stuart Robson, Auteur ; Stephen Kyle, Auteur ; Jan Boehm, Auteur Editeur : Berlin, New York : Walter de Gruyter Année de publication : 2020 Importance : 822 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-3-11-060724-6 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] analyse d'image numérique [Termes descripteurs IGN] étalonnage [Termes descripteurs IGN] image 3D [Termes descripteurs IGN] instrument de photogrammétrie [Termes descripteurs IGN] logiciel de photogrammétrie [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] orientation d'image [Termes descripteurs IGN] photogrammétrie métrologique [Termes descripteurs IGN] précision des mesures [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] transformation mathématique Index. décimale : 33.30 Photogrammétrie numérique Résumé : (Editeur) This is the third edition of the well-known guide to close-range photogrammetry. It provides a thorough presentation of the methods, mathematics, systems and applications which comprise the subject of close-range photogrammetry, which uses accurate imaging techniques to analyse the three-dimensional shape of a wide range of manufactured and natural objects. Note de contenu : 1. Introduction 2. Mathematical fundamentals 3. Imaging technology 4. Analytical methods 5. Digital image analysis 6. Measuring tasks and systems 7. Measurement design and quality 8. Example applications Numéro de notice : 26341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95700

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Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 70 - 83 Titre : Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours Type de document : Article/Communication Auteurs : David Griffiths, Auteur ; Jan Boehm, Auteur Année de publication : 2019 Article en page(s) : pp 70 - 83 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] bati [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données publiques [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] Royaume-Uni [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] zone rurale Résumé : (Auteur) Robust and reliable automatic building detection and segmentation from aerial images/point clouds has been a prominent field of research in remote sensing, computer vision and point cloud processing for a number of decades. One of the largest issues associated with deep learning methods is the high quantity of data required for training. To help address this we present a method to improve public GIS building footprint labels by using Morphological Geodesic Active Contours (MorphGACs). We demonstrate by improving the quality of building footprint labels for detection and semantic segmentation, more robust and reliable models can be obtained. We evaluate these methods over a large UK-based dataset of 24556 images containing 169835 building instances. This is achieved by training several Mask/Faster R-CNN and RetinaNet deep convolutional neural networks. Networks are supplied with both RGB and fused RGB-lidar data. We offer quantitative analysis on the benefits of the inclusion of depth data for building segmentation. By employing both methods we achieve a detection accuracy of 0.92 (mAP@0.5) and segmentation f1 scores of 0.94 over a 4911 test images ranging from urban to rural scenes. Numéro de notice : A2019-265 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.013 date de publication en ligne : 06/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.013 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93079 [article]

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The iQmulus urban showcase: automatic tree classification and identification in huge mobile mapping point clouds / Jan Boehm (2016)    

Public contenu dans 23th ISPRS international congress, 3. Commission III / Lena Halounova (2016)   Titre : The iQmulus urban showcase: automatic tree classification and identification in huge mobile mapping point clouds Type de document : Article/Communication Auteurs : Jan Boehm, Auteur ; Mathieu Brédif , Auteur ; T. Gierlinger, Auteur ; M. Krämer, Auteur ; R.E. Lindenberg, Auteur ; K. Liu, Auteur ; F. Michel, Auteur ; B. Sirmacek, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. XLI-B3 Projets : IQmulus / Métral, Claudine Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque Archives Commission 3 Importance : pp 301 - 307 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données massives [Termes descripteurs IGN] Spark [Termes descripteurs IGN] Toulouse [Termes descripteurs IGN] traitement de données localisées Résumé : (auteur) Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study, the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow. Numéro de notice : C2016-041 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B3-301-2016 date de publication en ligne : 09/06/2016 En ligne : https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91853

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Stereo-imaging network design for precise and dense 3D reconstruction / Ali Hosseininaveh Ahmadabadian in Photogrammetric record, vol 29 n° 147 (September - November 2014)  

Public [article]  inPhotogrammetric record > vol 29 n° 147 (September - November 2014) . - pp 317 - 336 Titre : Stereo-imaging network design for precise and dense 3D reconstruction Type de document : Article/Communication Auteurs : Ali Hosseininaveh Ahmadabadian, Auteur ; S. Robson, Auteur ; Jan Boehm, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 317 - 336 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes descripteurs IGN] angle de visée [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] photogrammétrie terrestre [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] système de référence local Résumé : (Auteur) This paper presents a novel stereo-imaging network design strategy comprising four steps: datum definition, optimum distance estimation, candidate camera position generation, and lastly clustering and selection of vantage viewpoints. An approximate 3D mesh model of an object from a quick low-cost system is used in the first step of the method. An ellipsoid is fitted to the rough model in order to find a suitable imaging distance regarding range- and visibility-related constraints for a stereopair. Next, a set of viewpoints is provided taking into account the ellipsoid and the calculated distance. Finally, camera positions and orientations are defined by clustering and choosing the vantage viewpoints. A system, called “imaging network designer” (IND), has been developed based on this method and is evaluated with two different tests. Results show that IND can provide an imaging network, which fulfils the expected precision for the entire object. Numéro de notice : A2014-491 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12076 date de publication en ligne : 18/09/2014 En ligne : https://doi.org/10.1111/phor.12076 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74080 [article]

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A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs / Ali Hosseininaveh Ahmadabadian in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 78 (April 2013) . - pp 157 - 167 Titre : A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs Type de document : Article/Communication Auteurs : Ali Hosseininaveh Ahmadabadian, Auteur ; Stuart Robson, Auteur ; Jan Boehm, Auteur Année de publication : 2013 Article en page(s) : pp 157 - 167 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] chambre de prise de vue numérique [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] contrainte géométrique [Termes descripteurs IGN] échelle [Termes descripteurs IGN] espace objet [Termes descripteurs IGN] instrumentation Nikon [Termes descripteurs IGN] MicMac [Termes descripteurs IGN] précision stéréoscopique [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] reconstruction d'objet [Termes descripteurs IGN] semis de points Résumé : (Auteur) Photogrammetric methods for dense 3D surface reconstruction are increasingly available to both professional and amateur users who have requirements that span a wide variety of applications. One of the key concerns in choosing an appropriate method is to understand the achievable accuracy and how choices made within the workflow can alter that outcome. In this paper we consider accuracy in two components: the ability to generate a correctly scaled 3D model; and the ability to automatically deliver a high quality data set that provides good agreement to a reference surface. The determination of scale information is particularly important, since a network of images usually only provides angle measurements and thus leads to unscaled geometry. A solution is the introduction of known distances in object space, such as base lines between camera stations or distances between control points. In order to avoid using known object distances, the method presented in this paper exploits a calibrated stereo camera utilizing the calibrated base line information from the camera pair as an observational based geometric constraint. The method provides distance information throughout the object volume by orbiting the object. In order to test the performance of this approach, four topical surface matching methods have been investigated to determine their ability to produce accurate, dense point clouds. The methods include two versions of Semi-Global Matching as well as MicMac and Patch-based Multi-View Stereo (PMVS). These methods are implemented on a set of stereo images captured from four carefully selected objects by using (1) an off-the-shelf low cost 3D camera and (2) a pair of Nikon D700 DSLR cameras rigidly mounted in close proximity to each other. Inter-comparisons demonstrate the subtle differences between each of these permutations. The point clouds are also compared to a dataset obtained with a Nikon MMD laser scanner. Finally, the established process of achieving accurate point clouds from images and known object space distances are compared with the presented strategies. Results from the matching demonstrate that if a good imaging network is provided, using a stereo camera and bundle adjustment with geometric constraints can effectively resolve the scale. Among the strategies for dense 3D reconstruction, using the presented method for solving the scale problem and PMVS on the images captured with two DSLR cameras resulted in a dense point cloud as accurate as the Nikon laser scanner dataset. Numéro de notice : A2013-184 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32322 [article]

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vol 76 - February 2013 - Special issue: Terrestrial 3D modelling (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / International society for photogrammetry and remote sensing  

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