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BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)
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Titre : BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model Type de document : Article/Communication Auteurs : Debaditya Acharya, Auteur ; Milad Ramezani, Auteur ; Kourosh Khoshelham, Auteur ; Stephan Winter, Auteur Année de publication : 2019 Article en page(s) : pp 157 - 171 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme de Gauss-Newton
[Termes IGN] appariement de données localisées
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] estimation de pose
[Termes IGN] filtre de Kalman
[Termes IGN] longueur focale
[Termes IGN] Matlab
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle 3D du site
[Termes IGN] positionnement en intérieur
[Termes IGN] trajet (mobilité)Résumé : (Auteur) This article presents an accurate and robust visual indoor localisation approach that not only is infrastructure-free, but also avoids accumulation error by taking advantage of (1) the widespread ubiquity of mobile devices with cameras and (2) the availability of 3D building models for most modern buildings. Localisation is performed by matching image sequences captured by a camera, with a 3D model of the building in a model-based visual tracking framework. Comprehensive evaluation of the approach with a photo-realistic synthetic dataset shows the robustness of the localisation approach under challenging conditions. Additionally, the approach is tested and evaluated on real data captured by a smartphone. The results of the experiments indicate that a localisation accuracy better than 10 cm can be achieved by using this approach. Since localisation errors do not accumulate the proposed approach is suitable for indoor localisation tasks for long periods of time and augmented reality applications, without requiring any local infrastructure. A MATLAB implementation can be found on https://github.com/debaditya-unimelb/BIM-Tracker. Numéro de notice : A2019-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.014 Date de publication en ligne : 27/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92473
in ISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 157 - 171[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Equivalent constraints for two-view geometry : Pose solution/pure rotation identification and 3D reconstruction / Qi Cai in International journal of computer vision, vol 127 n° 2 (February 2019)
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Titre : Equivalent constraints for two-view geometry : Pose solution/pure rotation identification and 3D reconstruction Type de document : Article/Communication Auteurs : Qi Cai, Auteur ; Yuanxin Wu, Auteur ; Lilian Zhang, Auteur ; Peike Zhang, Auteur Année de publication : 2019 Article en page(s) : pp 163 - 180 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] coplanarité
[Termes IGN] estimation de pose
[Termes IGN] programmation par contraintes
[Termes IGN] reconstruction 3DRésumé : (Auteur) Two-view relative pose estimation and structure reconstruction is a classical problem in computer vision. The typical methods usually employ the singular value decomposition of the essential matrix to get multiple solutions of the relative pose, from which the right solution is picked out by reconstructing the three-dimension (3D) feature points and imposing the constraint of positive depth. This paper revisits the two-view geometry problem and discovers that the two-view imaging geometry is equivalently governed by a Pair of new Pose-Only (PPO) constraints: the same-side constraint and the intersection constraint. From the perspective of solving equation, the complete pose solutions of the essential matrix are explicitly derived and we rigorously prove that the orientation part of the pose can still be recovered in the case of pure rotation. The PPO constraints are simplified and formulated in the form of inequalities to directly identify the right pose solution with no need of 3D reconstruction and the 3D reconstruction can be analytically achieved from the identified right pose. Furthermore, the intersection inequality also enables a robust criterion for pure rotation identification. Experiment results validate the correctness of analyses and the robustness of the derived pose solution/pure rotation identification and analytical 3D reconstruction. Numéro de notice : A2018-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1136-9 Date de publication en ligne : 30/11/2018 En ligne : https://doi.org/10.1007/s11263-018-1136-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92526
in International journal of computer vision > vol 127 n° 2 (February 2019) . - pp 163 - 180[article]The orthographic projection model for pose calibration of long focal images / Laura F. Julià in IPOL Journal, Image Processing On Line, vol 9 (2019)
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Titre : The orthographic projection model for pose calibration of long focal images Type de document : Article/Communication Auteurs : Laura F. Julià, Auteur ; Pascal Monasse, Auteur ; Marc Pierrot-Deseilligny , Auteur
Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 232 - 250 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] estimation de pose
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] longueur focale
[Termes IGN] Matlab
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] projection orthographique
[Termes IGN] structure-from-motionRésumé : (auteur) Most stereovision and Structure from Motion (SfM) methods rely on the pinhole camera model based on perspective projection. From this hypothesis the fundamental matrix and the epipolar constraints are derived, which are the milestones of pose estimation. In this article we present a method based on the matrix factorization due to Tomasi and Kanade that relies on a simpler camera model, resulting in orthographic projection. This method can be used for the pose estimation of perspective cameras in configurations where other methods fail, in particular, when using cameras with long focal length lenses. We show this projection is an approximation of the pinhole camera model when the camera is far away from the scene. The performance of our implementation of this pose estimation method is compared to that given by the perspective-based methods for several configurations using both synthetic and real data. We show through some examples and experiments that the accuracy achieved and the robustness of this method make it worth considering in any SfM procedure. Numéro de notice : A2019-643 Affiliation des auteurs : LASTIG LOEMI (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5201/ipol.2019.248 Date de publication en ligne : 05/09/2019 En ligne : https://doi.org/10.5201/ipol.2019.248 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96175
in IPOL Journal, Image Processing On Line > vol 9 (2019) . - pp 232 - 250[article]
Titre : Geometric camera pose refinement with learned depth maps Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Cédric Demonceaux, Auteur ; Valérie Gouet-Brunet
, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : ICIP 2019, 26th IEEE International Conference on Image Processing 22/09/2019 25/09/2019 Taipei Taiwan Proceedings IEEE Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme ICP
[Termes IGN] carte de profondeur
[Termes IGN] estimation de pose
[Termes IGN] réseau neuronal convolutif
[Termes IGN] scène intérieure
[Termes IGN] semis de pointsRésumé : (auteur) We present a new method for image-only camera relocalisation composed of a fast image indexing retrieval step followed by pose refinement based on ICP (Iterative Closest Point). The first step aims to find an initial pose for the query by evaluating images similarity with low dimensional global deep descriptors. Subsequently, we predict with a fully convolutional deep encoder-decoder neural network a dense depth map from the image query. We use this depth map to create a local point cloud and refine the initial query pose using an ICP algorithm.We demonstrate the effectiveness of our new approach on various indoor scenes. Compared to learned pose regression methods, our proposal can be used on multiple scenes without the need of a specific weights-setup for each scene, while showing equivalent results. Numéro de notice : C2019-015 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2019.8803014 Date de publication en ligne : 26/08/2019 En ligne : https://doi.org/10.1109/ICIP.2019.8803014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93279
Titre : Perspective-n-learned-point: pose estimation from relative depth Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Cédric Demonceaux, Auteur ; Valérie Gouet-Brunet
, Auteur
Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : BMVC 2019, British Machine Vision Conference 09/09/2019 12/09/2019 Cardiff Royaume-Uni OA Proceedings Importance : 15 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de profondeur
[Termes IGN] classification par réseau neuronal
[Termes IGN] estimation de pose
[Termes IGN] géométrie de l'image
[Termes IGN] recherche d'image basée sur le contenuRésumé : (Auteur) In this paper we present an online camera pose estimation method that combines Content-Based Image Retrieval (CBIR) and pose refinement based on a learned representation of the scene geometry extracted from monocular images. Our pose estimation method is two-step, we first retrieve an initial 6 Degrees of Freedom (DoF) location of an unknown-pose query by retrieving the most similar candidate in a pool of geo-referenced images. In a second time, we refine the query pose with a Perspective-n-Point (PnP) algorithm where the 3D points are obtained thanks to a generated depth map from the retrieved image candidate. We make our method fast and lightweight by using a common neural network architecture to generate the image descriptor for image indexing and the depth map used to create the 3D points required in the PnP pose refinement step. We demonstrate the effectiveness of our proposal through extensive experimentation on both indoor and outdoor scenes, as well as generalisation capability of our method to unknown environment. Finally, we show how to deploy our system even if geometric information is missing to train our monocular-image-to-depth neural networks. Numéro de notice : C2019-025 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans Date de publication en ligne : 12/11/2019 En ligne : https://bmvc2019.org/wp-content/uploads/papers/0981-paper.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94320 Documents numériques
en open access
Perspective-n-learned-point ... - pdf auteurAdobe Acrobat PDFStructure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)
PermalinkVision-based localization with discriminative features from heterogeneous visual data / Nathan Piasco (2019)
PermalinkDepth-based hand pose estimation : Methods, data, and challenges / James Steven Supančič in International journal of computer vision, vol 126 n° 11 (November 2018)
Permalink3D urban geovisualization: in situ augmented and mixed reality experiments / Alexandre Devaux in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-4 (October 2018)
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PermalinkImage-based synthesis for deep 3D human pose estimation / Grégory Rogez in International journal of computer vision, vol 126 n° 9 (September 2018)
PermalinkLandmark based localization in urban environment / Xiaozhi Qu in ISPRS Journal of photogrammetry and remote sensing, vol 140 (June 2018)
PermalinkSDF-2-SDF registration for real-time 3D reconstruction from RGB-D data / Miroslava Slavcheva in International journal of computer vision, vol 126 n° 6 (June 2018)
PermalinkReal-time accurate 3D head tracking and pose estimation with consumer RGB-D cameras / David Joseph Tan in International journal of computer vision, vol 126 n° 2-4 (April 2018)
PermalinkA survey on visual-based localization : on the benefit of heterogeneous data / Nathan Piasco in Pattern recognition, vol 74 (February 2018)
PermalinkAdéquation algorithme architecture pour la localisation basée image sur système embarqué / David Vandergucht (2018)
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