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Auteur Xuming Ge |
Documents disponibles écrits par cet auteur (7)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesTarget-based automated matching of multiple terrestrial laser scans for complex forest scenes / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
Titre : Target-based automated matching of multiple terrestrial laser scans for complex forest scenes Type de document : Article/Communication Auteurs : Xuming Ge, Auteur ; Qing Zhu, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 13 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de données localisées
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] densité de la végétation
[Termes IGN] détection d'arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] scène forestière
[Termes IGN] semis de pointsRésumé : (Auteur) Terrestrial laser scanners are widely used to derive unbiased and non-destructive estimates of the vertical distribution of the plant area index and plant area volume density at plot-level scales, as well as the above-ground biomass, height, and diameter at breast height of individual trees. Multiple scans are often employed to capture and register data so that all of the stems can be detected and their complete forms can be analyzed. Researchers have traditionally preferred target-less strategies to register scans because of their low cost and convenience. However, in complex forest scenes, even state-of-the-art approaches cannot guarantee the success of any pairwise registration. In this study, we present an automated target-based processing approach for the registration of unordered scans in complex forest scenes. In contrast to previous studies, the proposed registration method automatically detects the artificial targets and builds a geometric network to judge their connectivity. A pose graph is then exploited to combine these data with the corresponding pairwise transformation, and then the scans are integrated into a unified coordinate system. This method is more robust and efficient than target-less approaches because it is independent of the characteristics of individual trees and does not require ground information. In an experimental scenario, we use an extremely complex wild bamboo forest scene to evaluate the performance of the proposed approach in terms of robustness, accuracy, and efficiency. Numéro de notice : A2021-573 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.019 Date de publication en ligne : 15/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98173
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 1 - 13[article]Réservation
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Titre : Object-based incremental registration of terrestrial point clouds in an urban environment Type de document : Article/Communication Auteurs : Xuming Ge, Auteur ; Han Hu, Auteur Année de publication : 2020 Article en page(s) : pp 218 - 232 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] compensation par moindres carrés
[Termes IGN] conception orientée objet
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] primitive géométrique
[Termes IGN] scène urbaine
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) Registration of terrestrial point clouds is essential for large-scale urban applications. The robustness, accuracy, and runtime are generally given the highest priority in the design of appropriate algorithms. Most approaches that target general scenarios can only fulfill some of these factors, that is, robustness and accuracy come at the cost of increased runtime and vice versa. This paper proposes an object-based incremental registration strategy that accomplishes all of these objectives without the need for artificial targets, aiming at a specific scenario, the urban environment. The key is to decompose the degrees of freedom for the SE(3) transformation to three separate but closely related steps, considering that scanners are generally leveled in urban scenes: (1) 2D transformation with matches from line primitives, (2) vertical offset compensation by robust least-squares optimization, and (3) full SE(3) least-squares refinement using uniformly selected local patches. The robustness is prioritized in the whole pipeline, as structured first by a primitive-based registration and two least-squares optimizations with robust estimations that do not require specific keypoints. An object-based strategy for terrestrial point clouds is used to increase the reliability of the first step by the line primitives, which significantly reduces the search space without affecting the recall ratio. The least-squares optimization contributes to achieve a global optimum for the accurate registration. The three coupling steps are also more efficient than segregated coarse-to-fine registration. Experimental evaluations for point clouds acquired in both a metropolis and in old-style cities reveal that the proposed methods are superior to or on par with the state-of-the-art in robustness, accuracy, and runtime. In addition, the methods are also agnostic to the primitives adopted. Numéro de notice : A2020-066 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.020 Date de publication en ligne : 29/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94584
in ISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 218 - 232[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Enhanced 3D mapping with an RGB-D sensor via integration of depth measurements and image sequences Type de document : Article/Communication Auteurs : Bo Wu, Auteur ; Xuming Ge, Auteur ; Linfu Xie, Auteur ; Wu Chen, Auteur Année de publication : 2019 Article en page(s) : pp 633 - 642 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'intérieur
[Termes IGN] carte de profondeur
[Termes IGN] cartographie 3D
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] données localisées 3D
[Termes IGN] état de l'art
[Termes IGN] image RVB
[Termes IGN] intégration de données
[Termes IGN] modélisation 3D
[Termes IGN] semis de points
[Termes IGN] séquence d'images
[Termes IGN] structure-from-motionRésumé : (Auteur) State-of-the-art visual simultaneous localization and mapping (SLAM) techniques greatly facilitate three-dimensional (3D) mapping and modeling with the use of low-cost red-green-blue-depth (RGB-D) sensors. However, the effective range of such sensors is limited due to the working range of the infra-red (IR) camera, which provides depth information, and thus the practicability of such sensors in 3D mapping and modeling is limited. To address this limitation, we present a novel solution for enhanced 3D mapping using a low-cost RGB-D sensor. We carry out state-of-the-art visual SLAM to obtain 3D point clouds within the mapping range of the RGB-D sensor and implement an improved structure-from-motion (SfM) on the collected RGB image sequences with additional constraints from the depth information to produce image-based 3D point clouds. We then develop a feature-based scale-adaptive registration to merge the gained point clouds to further generate enhanced and extended 3D mapping results. We use two challenging test sites to examine the proposed method. At these two sites, the coverage of both generated 3D models increases by more than 50% with the proposed solution. Moreover, the proposed solution achieves a geometric accuracy of about 1% in a measurement range of about 20 m. These positive experimental results not only demonstrate the feasibility and practicality of the proposed solution but also its potential. Numéro de notice : A2019-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.9.633 Date de publication en ligne : 01/09/2019 En ligne : https://doi.org/10.14358/PERS.85.9.633 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93542
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 9 (September 2019) . - pp 633 - 642[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019091 SL Revue Centre de documentation Revues en salle Disponible
Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification
[Termes IGN] classification basée sur les régions
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Hong-Kong
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] Paris (75)
[Termes IGN] scène urbaine
[Termes IGN] segmentation en régions
[Termes IGN] segmentation hiérarchique
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075
in ISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165[article]Réservation
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Titre : Non-rigid registration of 3D point clouds under isometric deformation Type de document : Article/Communication Auteurs : Xuming Ge, Auteur Année de publication : 2016 Article en page(s) : pp 192 – 202 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] alignement
[Termes IGN] déformation géométrique
[Termes IGN] image 3D
[Termes IGN] isométrie
[Termes IGN] semis de pointsRésumé : (Auteur) An algorithm for pairwise non-rigid registration of 3D point clouds is presented in the specific context of isometric deformations. The critical step is registration of point clouds at different epochs captured from an isometric deformation surface within overlapping regions. Based on characteristics invariant under isometric deformation, a variant of the four-point congruent sets algorithm is applied to generate correspondences between two deformed point clouds, and subsequently a RANSAC framework is used to complete cluster extraction in preparation for global optimal alignment. Examples are presented and the results compared with existing approaches to demonstrate the two main contributions of the technique: a success rate for generating true correspondences of 90% and a root mean square error after final registration of 2–3 mm. Numéro de notice : A2016--018 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.09.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.09.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83880
in ISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 192 – 202[article]PermalinkPermalink
