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Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)
[article]
Titre : Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization Type de document : Article/Communication Auteurs : Jiali Han, Auteur ; Mengqi Rong, Auteur ; Hanqing Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 57 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire de Markov
[Termes IGN] données lidar
[Termes IGN] espace intérieur
[Termes IGN] maillage
[Termes IGN] programmation linéaire
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] vectorisationRésumé : (Auteur) Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Field (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches. Numéro de notice : A2021-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.019 Date de publication en ligne : 15/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97779
in ISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 57 - 74[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021071 SL Revue Centre de documentation Revues en salle Disponible 081-2021073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Fast weakly supervised detection of railway-related infrastructures in lidar acquisitions / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
[article]
Titre : Fast weakly supervised detection of railway-related infrastructures in lidar acquisitions Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Jean-Philippe Riant, Auteur ; Jean-Christophe Michelin , Auteur ; Sofia Costa d’Aguiar, Auteur Année de publication : 2021 Conférence : ISPRS 2021, Commission 2, 24th ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Annals Commission 2 Article en page(s) : pp 27 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] réseau ferroviaire
[Termes IGN] segmentationRésumé : (auteur) Railroad environments are peculiar, as they combine dense urban areas, along with rural parts. They also display a very specific spatial organization. In order to monitor a railway network a at country scale, LiDAR sensors can be equipped on a running train, performing a full acquisition of the network. Then most processing steps are manually done. In this paper, we propose to improve performances and production flow by creating a classification of the acquired data. However, there exists no public benchmark, and little work on LiDAR data classification in railroad environments. Thus, we propose a weakly supervised method for the pointwise classification of such data. We show that our method can be improved by using the l0-cut pursuit algorithm and regularize the noisy pointwise classification on the produced segmentation. As production is envisaged in our context, we designed our implementation such that it is computationally efficient. We evaluate our results against a manual classification, and show that our method can reach a FScore of 0.96 with just a few samples of each class. Numéro de notice : A2021-615 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2021-27-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-2-2021-27-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97953
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 27 - 34[article]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)
[article]
Titre : Individual tree extraction from UAV lidar point clouds based on self-adaptive mean shift segmentation Type de document : Article/Communication Auteurs : Zhenyang Hui, Auteur ; N. Li, Auteur ; Y. Xia, Auteur ; Penggen Cheng, Auteur ; Y. He, Auteur Année de publication : 2021 Conférence : ISPRS 2021, Commission 1, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 1 Article en page(s) : pp 25 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de décalage moyen
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction d'arbres
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Unman aerial vehicle (UAV) LiDAR has been widely used in the field of forestry. Individual tree extraction is a key step for forest inventory. Although many individual tree extraction methods have been proposed, the individual tree extraction accuracy is still low due to the complex forest environments. Moreover, many parameters in these methods generally need to be set. Thus, the degree of automation of the methods is generally low. To solve these problems, this paper proposed an automatic mean shift segmentation method, in which the kernel bandwidths can be calculated self-adaptively. Meanwhile, a hierarchy mean shift segmentation technique was proposed to extract individual tree gradually. A plot-level UAV LiDAR tree dataset was adopted for testing the performance of the proposed method. Experimental results showed that the proposed method can achieve better individual tree extraction result without any parameter setting. Compared with the traditional mean shift segmentation method, both the completeness and mean accuracy of the proposed method are higher. Numéro de notice : A2021-318 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-1-2021-25-2021 Date de publication en ligne : 17/06/2021 En ligne : https://doi.org/10.5194/isprs-annals-V-1-2021-25-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97950
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2021 (July 2021) . - pp 25 - 30[article]Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image / Genki Takahashi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
[article]
Titre : Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image Type de document : Article/Communication Auteurs : Genki Takahashi, Auteur ; H. Masuda, Auteur Année de publication : 2021 Article en page(s) : pp 67 - 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] alignement d'arbres
[Termes IGN] apprentissage automatique
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction d'arbres
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] route
[Termes IGN] semis de points
[Termes IGN] Tokyo (Japon)
[Termes IGN] zone urbaineRésumé : (auteur) Efficient management of roadside trees for local governments is important. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner has the possibility to be applied to estimate DBH of roadside trees using point clouds. In this study, we propose a method for detecting roadside trees and estimating their DBHs automatically from MMS point clouds. In our method, point clouds captured using the MMS are mapped on a 2D image plane, and they are converted into a wireframe model by connecting adjacent points. Then, geometric features are calculated for each point in the wireframe model. Tree points are detected using a machine learning technique. The DBH of each tree is calculated using vertically aligned circles extracted from the wireframe model. Our method allows robustly calculating the DBH even if there is a hump at breast height. We evaluated our method using actual MMS data measured in an urban area in Tokyo. Our method achieved a high extraction performance of 100 percent of precision and 95.1 percent of recall for 102 roadside trees. The average accuracy of the DBH was 2.0 cm. These results indicate that our method is useful for the efficient management of roadside trees. Numéro de notice : A2021-491 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2021-67-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-2-2021-67-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97956
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 67 - 74[article]Towards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
[article]
Titre : Towards efficient indoor/outdoor registration using planar polygons Type de document : Article/Communication Auteurs : Rahima Djahel, Auteur ; Bruno Vallet , Auteur ; Pascal Monasse, Auteur Année de publication : 2021 Projets : BIOM / Vallet, Bruno Article en page(s) : pp 51 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] appariement de primitives
[Termes IGN] bati
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de points
[Termes IGN] géométrie euclidienne
[Termes IGN] polygone
[Termes IGN] scène intérieure
[Termes IGN] scène urbaine
[Termes IGN] superposition de donnéesRésumé : (auteur) The registration of indoor and outdoor scans with a precision reaching the level of geometric noise represents a major challenge for Indoor/Outdoor building modeling. The basic idea of the contribution presented in this paper consists in extracting planar polygons from indoor and outdoor LiDAR scans, and then matching them. In order to cope with the very small overlap between indoor and outdoor scans of the same building, we propose to start by extracting points lying in the buildings’ interior from the outdoor scans as points where the laser ray crosses detected façades. Since, within a building environment, most of the objects are bounded by a planar surface, we propose a new registration algorithm that matches planar polygons by clustering polygons according to their normal direction, then by their offset in the normal direction. We use this clustering to find possible polygon correspondences (hypotheses) and estimate the optimal transformation for each hypothesis. Finally, a quality criteria is computed for each hypothesis in order to select the best one. To demonstrate the accuracy of our algorithm, we tested it on real data with a static indoor acquisition and a dynamic (Mobile Laser Scanning) outdoor acquisition. Numéro de notice : A2021-490 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2021-51-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-2-2021-51-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97955
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 51 - 58[article]3D reconstruction of bridges from airborne laser scanning data and cadastral footprints / Steffen Goebbels in Journal of Geovisualization and Spatial Analysis, vol 5 n° 1 (June 2021)PermalinkAn innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data / Van-Tho Nguyen in Annals of Forest Science, vol 78 n° 2 (June 2021)PermalinkIndividual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data / Haijian Liu in Remote sensing of environment, vol 258 (June 2021)PermalinkModel-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing / Michael L. Benson in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkPredicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)PermalinkTree height growth modelling using LiDAR-derived topography information / Milan Kobal in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)PermalinkWeak relationships of continuous forest management intensity and remotely sensed stand structural complexity in temperate mountain forests / Thomas Asbeck in European Journal of Forest Research, vol 140 n° 3 (June 2021)PermalinkIntegration of laser scanner and photogrammetry for heritage BIM enhancement / Yahya Alshawabkeh in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkSensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment / Maxime Soma in Remote sensing of environment, vol 257 (May 2021)PermalinkDetecting archaeological features with airborne laser scanning in the alpine tundra of Sápmi, Northern Finland / Oula Seitsonen in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkAn iterative-mode scan design of terrestrial laser scanning in forests for minimizing occlusion effects / Linyuan Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkAutomated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours / Amir Hossein Safaie in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkTree extraction and estimation of walnut structure parameters using airborne LiDAR data / Javier Estornell in International journal of applied Earth observation and geoinformation, vol 96 (April 2021)PermalinkTerrestrial laser scanning intensity captures diurnal variation in leaf water potential / S. Junttila in Remote sensing of environment, Vol 255 (March 2021)Permalink3D change detection using adaptive thresholds based on local point cloud density / Dan Liu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkEnhanced trajectory estimation of mobile laser scanners using aerial images / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkImproving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])PermalinkOntology-based semantic conceptualisation of historical built heritage to generate parametric structured models from point clouds / Elisabetta Colucci in Applied sciences, vol 11 n° 6 (March 2021)PermalinkProgressive TIN densification with connection analysis for urban Lidar data / Tao Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)PermalinkWhat factors shape spatial distribution of biomass in riparian forests? Insights from a LiDAR survey over a large area / Leo Huylenbroeck in Forests, vol 12 n° 3 (March 2021)PermalinkAn anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkAutomatic filtering and 2D modeling of airborne laser scanning building point cloud / Fayez Tarsha-Kurdi in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkCurved buildings reconstruction from airborne LiDAR data by matching and deforming geometric primitives / Jingwei Song in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkDeveloping a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)PermalinkA feature-preserving point cloud denoising algorithm for LiDAR-derived DEM construction / Chuanfa Chen in Survey review, Vol 53 n° 377 (February 2021)PermalinkImproving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkMonitoring the coastal changes of the Po river delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: implications for coastline changes in 2100 A.D. / Massimo Fabris in Remote sensing, Vol 13 n° 3 (February 2021)PermalinkTropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkA density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkPermalink3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)PermalinkApplications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)PermalinkApport des méthodes : imagerie drone, LiDAR et imagerie hyperspectrale pour l’étude du littoral vendéen / Mathis Baudis (2021)PermalinkPermalinkAutomatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)PermalinkPermalinkBuilding extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)PermalinkCalcul de la largeur à pleins bords de grands cours d’eau à partir de MNT LiDAR / Nicolas Fermen (2021)PermalinkContributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)PermalinkConvex hull: another perspective about model predictions and map derivatives from remote sensing data / Jean-Pierre Renaud (2021)PermalinkCorrection radiométrique et recalage de nuages de points pour la reconstruction tridimensionnelle d'oeuvres du patrimoine culturel / Nathan Sanchiz (2021)PermalinkDeep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)PermalinkDétection d’ouvertures par segmentation sémantique de nuages de points 3D : apport de l’apprentissage profond / Camille Lhenry (2021)PermalinkDétection/reconnaissance d'objets urbains à partir de données 3D multicapteurs prises au niveau du sol, en continu / Younes Zegaoui (2021)PermalinkDétection et reconstruction 3D d’arbres urbains par segmentation de nuages de points : apport de l’apprentissage profond / Victor Alteirac (2021)Permalink