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Object-based incremental registration of terrestrial point clouds in an urban environment / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 218 - 232 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 descripteurs IGN] compensation par moindres carrés [Termes descripteurs IGN] conception orientée objet [Termes descripteurs IGN] données laser [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] primitive géométrique [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser terrestre Résumé : (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 [article]

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Enhanced 3D mapping with an RGB-D sensor via integration of depth measurements and image sequences / Bo Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 9 (September 2019) . - pp 633 - 642 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 descripteurs IGN] carte d'intérieur [Termes descripteurs IGN] carte de profondeur [Termes descripteurs IGN] cartographie 3D [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] séquence d'images [Termes descripteurs IGN] Simultaneous Localisation And Mapping [Termes descripteurs IGN] structure-from-motion Résumé : (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 [article]

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Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165 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 descripteurs IGN] champ aléatoire conditionnel [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification basée sur les régions [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] modèle 3D de l'espace urbain [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (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 [article]

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Non-rigid registration of 3D point clouds under isometric deformation / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 192 – 202 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 descripteurs IGN] algorithme de la plus proche paire [Termes descripteurs IGN] alignement [Termes descripteurs IGN] déformation géométrique [Termes descripteurs IGN] image 3D [Termes descripteurs IGN] isométrie [Termes descripteurs IGN] semis de points Résumé : (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 En ligne : http://dx.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 [article]

Surface-based matching of 3D point clouds with variable coordinates in source and target system / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 111 (January 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 111 (January 2016) . - pp 1 – 12 Titre : Surface-based matching of 3D point clouds with variable coordinates in source and target system Type de document : Article/Communication Auteurs : Xuming Ge, Auteur ; Thomas Wunderlich, Auteur Année de publication : 2016 Article en page(s) : pp 1 – 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] compensation par moindres carrés [Termes descripteurs IGN] image multicapteur [Termes descripteurs IGN] modèle de Gauss-Helmert [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] semis de points Résumé : (auteur) The automatic co-registration of point clouds, representing three-dimensional (3D) surfaces, is an important technique in 3D reconstruction and is widely applied in many different disciplines. An alternative approach is proposed here that estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface. The approach uses the nonlinear Gauss–Helmert model, minimizing the quadratically constrained least squares problem. This approach has the ability to match arbitrarily oriented 3D surfaces captured from a number of different sensors, on different time-scales and at different resolutions. In addition to the 3D surface-matching paths, the mathematical model allows the precision of the point clouds to be assessed after adjustment. The error behavior of surfaces can also be investigated based on the proposed approach. Some practical examples are presented and the results are compared with the iterative closest point and the linear least-squares approaches to demonstrate the performance and benefits of the proposed technique. Numéro de notice : A2016-036 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://www.sciencedirect.com/science/article/pii/S0924271615002427 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79514 [article]

Target identification in terrestrial laser scanning / Xuming Ge in Survey review, vol 47 n° 341 (March 2015)  

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