Détail de l'auteur
Auteur Michael Weinmann |
Documents disponibles écrits par cet auteur (5)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesvol V-1-2021 - July 2021 - [Actes] XXIV ISPRS Congress, Commission 1 (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas Paparoditis
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- Preface: the 2021 edition of the XXIVth ISPRS congress / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2021 (July 2021)
- Individual tree extraction from UAV lidar point clouds based on self-adaptive mean shift segmentation / Zhenyang Hui in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2021 (July 2021)
est un bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences / International society for photogrammetry and remote sensing (1980 -) (2012 - )
Titre : vol V-1-2020 - August 2020 - [Actes] XXIV ISPRS virtual Congress, Commission 1, 31th August-2nd September 2020, Nice, France Type de document : Périodique Auteurs : Nicolas Paparoditis Année de publication : 2020 Conférence : ISPRS 2020, Commission 1, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France ISPRS OA Annals Commission 1 Langues : Anglais (eng) Numéro de notice : sans Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Nature : Numéro de périodique nature-HAL : DirectOuvrColl/Actes En ligne : https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-1-2020/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=32715 [n° ou bulletin]Albedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)
Titre : Albedo estimation for real-time 3D reconstruction using RGB-D and IR data Type de document : Article/Communication Auteurs : Patrick Stotko, Auteur ; Michael Weinmann, Auteur ; Reinhard Klein, Auteur Année de publication : 2019 Article en page(s) : pp 213 - 225 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] albedo
[Termes IGN] image infrarouge
[Termes IGN] image RVB
[Termes IGN] longueur d'onde
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] reconstruction 3D
[Termes IGN] réflectance
[Termes IGN] segmentation d'image
[Termes IGN] temps réel
[Termes IGN] texture d'imageRésumé : (Auteur) Reconstructing scenes in real-time using low-cost sensors has gained increasing attention in recent research and enabled numerous applications in graphics, vision, and robotics. While current techniques offer a substantial improvement regarding the quality of the reconstructed geometry, the degree of realism of the overall appearance is still lacking as the reconstruction of accurate surface appearance is highly challenging due to the complex interplay of surface geometry, reflectance properties and surrounding illumination. We present a novel approach that allows the reconstruction of both the geometry and the spatially varying surface albedo of a scene from RGB-D and IR data obtained via commodity sensors. In comparison to previous approaches, our approach offers an improved robustness and a significant speed-up to even fulfill the real-time requirements. For this purpose, we exploit the benefits of scene segmentation to improve albedo estimation due to the resulting better segment-wise coupling of IR and RGB data that takes into account the wavelength characteristics of different materials within the scene. The estimated albedo is directly integrated into the dense volumetric reconstruction framework using a novel weighting scheme to generate high-quality results. In our evaluation, we demonstrate that our approach allows albedo capturing of complicated scenarios including complex, high-frequent and strongly varying lighting as well as shadows. Numéro de notice : A2019-141 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.018 Date de publication en ligne : 04/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.018 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92479
in ISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 213 - 225[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
Titre : Geometric features and their relevance for 3D point cloud classification Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; Boris Jutzi, Auteur ; Clément Mallet Année de publication : 2017 Projets : 1-Pas de projet / Conférence : ISPRS 2017, Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Annals Article en page(s) : pp 157 - 164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classe d'objets
[Termes IGN] classification
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage de données
[Termes IGN] étiquette de classe
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] interprétation automatique
[Termes IGN] semis de points
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Façade, Ground and Vegetation) is available. Numéro de notice : A2017-860 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-1-W1-157-2017 Date de publication en ligne : 30/05/2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89840
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-1/W1 (May 2017) . - pp 157 - 164[article]
Titre : A classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; Michael Weinmann, Auteur ; Clément Mallet Année de publication : 2017 Projets : IQmulus / Métral, Claudine Article en page(s) : pp 277 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de décalage moyen
[Termes IGN] arbre (flore)
[Termes IGN] classification
[Termes IGN] Delft (Pays-Bas)
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] voxel
[Termes IGN] zone urbaineRésumé : (auteur) In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as “tree points” and “other points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6 % are labeled as “tree points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h) Numéro de notice : A2017-140 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs9030277 Date de publication en ligne : 16/03/2017 En ligne : http://doi.org/10.3390/rs9030277 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84614
in Remote sensing > vol 9 n° 3 (March 2017) . - pp 277[article]
