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Auteur Cédric Demonceaux |
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Affiner la recherche Interroger des sources externesImproving image description with auxiliary modality for visual localization in challenging conditions / Nathan Piasco in International journal of computer vision, vol 128 n° inconnu ([01/09/2020])
Titre : Improving image description with auxiliary modality for visual localization in challenging conditions Type de document : Article/Communication Auteurs : Nathan Piasco Année de publication : 2020 Projets : PLaTINUM / Gouet-Brunet, Valérie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] localisation basée image
[Termes descripteurs IGN] localisation basée visionRésumé : (auteur) Image indexing for lifelong localization is a key component for a large panel of applications, including robot navigation, autonomous driving or cultural heritage valorization. The principal difficulty in long-term localization arises from the dynamic changes that affect outdoor environments. In this work, we propose a new approach for outdoor large scale image-based localization that can deal with challenging scenarios like cross-season, cross-weather and day/night localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We show through extensive evaluation that our method can improve localization performances, especially in challenging scenarios when the visual appearance of the scene has changed. Our method is able to leverage both visual and geometric clues from monocular images to create discriminative descriptors for cross-season localization and effective matching of images acquired at different time periods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Finally we extended our method to reflectance modality and we compare multi-modal descriptors respectively based on geometry, material reflectance and a combination of both. Numéro de notice : A2020-508 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-020-01363-6 date de publication en ligne : 28/08/2020 En ligne : https://doi.org/10.1007/s11263-020-01363-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95738
in International journal of computer vision > vol 128 n° inconnu [01/09/2020][article]
Titre : Moving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans Type de document : Article/Communication Auteurs : Teng Wu Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France) Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. XLIII-B2 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : pp 507 - 514 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 2D
[Termes descripteurs IGN] espace intérieur
[Termes descripteurs IGN] fusion de données
[Termes descripteurs IGN] objet mobile
[Termes descripteurs IGN] reconstruction 3DRésumé : (auteur) Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system). Numéro de notice : C2020-008 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2020-507-2020 date de publication en ligne : 12/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-507-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95659
Titre : Geometric camera pose refinement with learned depth maps Type de document : Article/Communication Auteurs : Nathan Piasco Congrès : ICIP 2019, 26th IEEE International Conference on Image Processing (22 - 25 septembre 2019; Taipei, Taiwan), Commanditaire Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] algorithme ICP
[Termes descripteurs IGN] carte de profondeur
[Termes descripteurs IGN] estimation de pose
[Termes descripteurs IGN] réseau neuronal convolutif
[Termes descripteurs IGN] scène intérieure
[Termes descripteurs 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+Ext (2020- ) 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 : Learning scene geometry for visual localization in challenging conditions Type de document : Article/Communication Auteurs : Nathan Piasco Congrès : ICRA 2019, International Conference on Robotics and Automation (20 - 24 mai 2019; Montréal, Québec - Canada) Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : pp 9094 - 9100 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] analyse d'image orientée objet
[Termes descripteurs IGN] analyse visuelle
[Termes descripteurs IGN] appariement d'images
[Termes descripteurs IGN] carte de profondeur
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] géométrie de l'image
[Termes descripteurs IGN] image RVB
[Termes descripteurs IGN] localisation basée vision
[Termes descripteurs IGN] précision de localisation
[Termes descripteurs IGN] prise de vue nocturne
[Termes descripteurs IGN] robotique
[Termes descripteurs IGN] scène urbaine
[Termes descripteurs IGN] variation diurne
[Termes descripteurs IGN] variation saisonnière
[Termes descripteurs IGN] vision par ordinateurRésumé : (auteur) We propose a new approach for outdoor large scale image based localization that can deal with challenging scenarios like cross-season, cross-weather, day/night and longterm localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We are able to increase recall@1 performances by 2.15% on cross-weather and long-term localization scenario and by 4.24% points on a challenging winter/summer localization sequence versus state-of-the-art methods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Numéro de notice : C2019-002 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICRA.2019.8794221 date de publication en ligne : 12/08/2019 En ligne : http://doi.org/10.1109/ICRA.2019.8794221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93774 Documents numériques
en open access
Learning scene geometry... - pdf auteurAdobe Acrobat PDF
Titre : Perspective-n-learned-point: pose estimation from relative depth Type de document : Article/Communication Auteurs : Nathan Piasco Congrès : BMVC 2019, British Machine Vision Conference (9 - 12 septembre 2009; University of Cardiff, Cardiff, Royaume-Uni) Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : 15 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] carte de profondeur
[Termes descripteurs IGN] classification par réseau neuronal
[Termes descripteurs IGN] estimation de pose
[Termes descripteurs IGN] géométrie de l'image
[Termes descripteurs 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+Ext (2020- ) 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 PDF
