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Termes IGN > 1-Candidats > semis de points
semis de points
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- Ensemble de points répartis de façon régulière ou quelconque sur une zone géographique donnée. (Glossaire de cartographie / CFC) Ces points peuvent être issus d'images ou de données lidar ...
Synonyme(s)nuage de pointsVoir aussi |
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Automatic registration of mobile mapping system Lidar points and panoramic-image sequences by relative orientation model / Ningning Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)
[article]
Titre : Automatic registration of mobile mapping system Lidar points and panoramic-image sequences by relative orientation model Type de document : Article/Communication Auteurs : Ningning Zhu, Auteur ; Bisheng Yang, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 913 - 922 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image panoramique
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] orientation relative
[Termes IGN] scène urbaine
[Termes IGN] semis de points
[Termes IGN] séquence d'images
[Termes IGN] superposition de données
[Termes IGN] SURF (algorithme)Résumé : (Auteur) To register mobile mapping system (MMS) lidar points and panoramic-image sequences, a relative orientation model of panoramic images (PROM) is proposed. The PROM is suitable for cases in which attitude or orientation parameters are unknown in the panoramic-image sequence. First, feature points are extracted and matched from panoramic-image pairs using the SURF algorithm. Second, these matched feature points are used to solve the relative attitude parameters in the PROM. Then, combining the PROM with the absolute position and attitude parameters of the initial panoramic image, the MMS lidar points and panoramic-image sequence are registered. Finally, the registration accuracy of the PROM method is assessed using corresponding points manually selected from the MMS lidar points and panoramic-image sequence. The results show that three types of MMS data sources are registered accurately based on the proposed registration method. Our method transforms the registration of panoramic images and lidar points into image feature-point matching, which is suitable for diverse road scenes compared with existing methods. Numéro de notice : A2021-899 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00006R2 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.14358/PERS.21-00006R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99298
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 12 (December 2021) . - pp 913 - 922[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021121 SL Revue Centre de documentation Revues en salle Disponible Detection of periodic displacements of shell structures with edges using spline surfaces, meshes and point clouds / Grzegorz Lenda in Reports on geodesy and geoinformatics, vol 112 n° 1 (December 2021)
[article]
Titre : Detection of periodic displacements of shell structures with edges using spline surfaces, meshes and point clouds Type de document : Article/Communication Auteurs : Grzegorz Lenda, Auteur ; Katarzyna Abrachamowicz, Auteur Année de publication : 2021 Article en page(s) : pp 27 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] déformation d'édifice
[Termes IGN] fonction spline
[Termes IGN] maillage
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) This research paper tackles the problem of determining displacements of complex-shaped shell structures, measured periodically using laser scanning. Point clouds obtained during different measurement epochs can be compared with each other directly or they can be converted into continuous models in the form of a triangle mesh or smooth patches (spline functions). The accuracy of the direct comparison of point clouds depends on the scanning density, while the accuracy of comparing the point cloud to the model depends on approximation errors that are formed during its creation. Modelling using triangle meshes flattens the local structure of the object compared to the spline model. However, if the shell has edges in its structure, their exact representation by spline models is impossible due to the undulations of functions along them. Edges can also be distorted by the mesh model by their chamfering with transverse triangles. These types of surface modelling errors can lead to the generation of pseudo-deformation of the structure, which is difficult to distinguish from real deformation. In order to assess the possibility of correct determination of deformation using the above-mentioned methods, laser scanning of a complex shell structure in two epochs was performed. Then, modelling and comparison of the results of periodic measurements were carried out. As a result of the research, advantages and disadvantages of each method were identified. It was noticed that none of the methods made it possible to correctly represent all deformations while suppressing pseudo-deformation. However, the combination of their best qualities made it possible to determine the actual deformation of the structure. Numéro de notice : A2021-962 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.2478/rgg-2021-0005 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.2478/rgg-2021-0005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100114
in Reports on geodesy and geoinformatics > vol 112 n° 1 (December 2021) . - pp 27 - 33[article]A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds / Yetao Yang in Computers & geosciences, vol 157 (December 2021)
[article]
Titre : A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Yetao Yang, Auteur ; Rongkui Tang, Auteur ; Jinglei Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104932 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] itération
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] structure hiérarchique de donnéesRésumé : (auteur) Airborne LiDAR point clouds classification has been a challenging task due to the characteristics of point clouds and the complexity of the urban environment. Recently, methods that directly act on unordered point set have achieved satisfactory results in point clouds classification. However, the existing methods that directly consume point clouds pay little attention to the interaction between the deep layers, which makes the feature learning insufficient in complex environments. In this paper, we propose a deep neural network for semantic labeling task. It iteratively learns deep features in a hierarchical structure, and provides a simple but efficient way to make interactions between different hierarchical levels. Since iteration process will greatly increase the number of layers, we employ the residual network to improve the performance. In addition, we also introduce dilated k-nearest neighbors and multi-scale grouping to increase the receptive field. The experiments on both Vaihingen 3D dataset and Dayton Annotated LiDAR Earth Scan (DALES) dataset demonstrate the effectiveness of the proposed method in point cloud classification. Numéro de notice : A2021-867 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104932 Date de publication en ligne : 04/09/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104932 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99098
in Computers & geosciences > vol 157 (December 2021) . - n° 104932[article]La photogrammétrie appliquée au récolement des réseaux enterrés : retour d’expérience d’une méthode industrialisée / Jérôme Leroux in XYZ, n° 169 (décembre 2021)
[article]
Titre : La photogrammétrie appliquée au récolement des réseaux enterrés : retour d’expérience d’une méthode industrialisée Type de document : Article/Communication Auteurs : Jérôme Leroux, Auteur ; Maxime Chauvin, Auteur ; Valentin Poitevin, Auteur Année de publication : 2021 Article en page(s) : pp 16 - 24 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement d'images
[Termes IGN] données localisées 3D
[Termes IGN] exploration de données géographiques
[Termes IGN] intelligence artificielle
[Termes IGN] portail
[Termes IGN] reconstruction 3D
[Termes IGN] réseau technique souterrain
[Termes IGN] semis de points
[Termes IGN] téléphone intelligent
[Termes IGN] téléphonie mobileRésumé : (Auteur) De nouvelles solutions existent aujourd’hui pour mesurer les réseaux enterrés. L’acquisition d’images avec un smartphone permet de reconstruire en 3D une fouille ouverte grâce aux algorithmes de photogrammétrie par corrélation dense. Les enjeux de fiabilité et d’industrialisation pour ce type de production de données nous ont poussés à améliorer une méthode d’acquisition préexistante. L’étude menée ici vise principalement à déterminer la meilleure technique de captation d’images. Les éléments clés de la réussite de la méthode reposent sur 1. L’utilisation de marqueurs dimensionnels à travers la pose des balises et 2. La détection de ces marqueurs dans les images via des algorithmes basés sur de l’Intelligence Artificielle. Les données 3D produites et accessibles via un portail web permettent de dessiner les infrastructures existantes en réduisant le plus possible l’intervention humaine. Numéro de notice : A2021-847 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99171
in XYZ > n° 169 (décembre 2021) . - pp 16 - 24[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2021041 RAB Revue Centre de documentation En réserve L003 Disponible Point clouds for use in Building Information Models (BIM) / Robert Klinc in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)
[article]
Titre : Point clouds for use in Building Information Models (BIM) Type de document : Article/Communication Auteurs : Robert Klinc, Auteur ; Uroš Jotanović, Auteur ; Klemen Kregar, Auteur Année de publication : 2021 Article en page(s) : pp 594 - 613 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canalisation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction automatique
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] mur
[Termes IGN] qualité du modèle
[Termes IGN] Revit
[Termes IGN] semis de points
[Termes IGN] traitement de semis de pointsRésumé : (Auteur) The use of point clouds in extracting data for building information modelling (BIM) has become common recently. Managers of older buildings are working to centralise information. Documentation about mechanical installations, plumbing, electricity, and previous interventions is often stored on scattered media, frequently still on paper. In the transformation of the material world into the digital world, the point cloud is the starting point, containing information about the material world obtained by various means such as photogrammetry, terrestrial or aerial laser scanning. Manual BIM modelling for management, maintenance and future use is a time-consuming and error-prone process. We would like to automate this process and avoid these errors. Recently, there have been developed an increasing number of stand-alone programmes and add-ons that provide automated, fast, and more accurate modelling based on point cloud data. In this paper, we present an investigation into the possibilities for automating the creation of BIM models from point cloud data. The result is a semi-automated process for modelling individual BIM elements, which we have tested on specific examples of modelling individual elements (walls, pipes, and columns). We note that despite the automation of the process, a high level of user interaction is still required to produce good quality models. Numéro de notice : A2021-931 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2021.04.594-613 Date de publication en ligne : 06/12/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.04.594-613 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99410
in Geodetski vestnik > vol 65 n° 4 (December 2021 - February 2022) . - pp 594 - 613[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2021041 RAB Revue Centre de documentation En réserve L003 Disponible The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkForest structural complexity tool: An open source, fully-automated tool for measuring forest point clouds / Sean Krisanski in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkA CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms / Ibrahim Fayad in Remote sensing of environment, vol 265 (November 2021)PermalinkDiffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkA method of extracting high-accuracy elevation control points from ICESat-2 altimetry data / Binbin Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkMobile mapping et PCRS / Clément Benoît in Géomatique expert, n° 136 (novembre - décembre 2021)PermalinkRobust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features / Bai Zhu in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkTowards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners / Berit Schmitz in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)PermalinkUsing LiDAR and Random Forest to improve deer habitat models in a managed forest landscape / Colin S. Shanley in Forest ecology and management, vol 499 (November-1 2021)PermalinkA vector-based method for drainage network analysis based on LiDAR data / Fangzheng Lyu in Computers & geosciences, vol 156 (November 2021)PermalinkAutomatic detection of planted trees and their heights using photogrammetric rpa point clouds / Kênia Samara Mourão Santos in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])PermalinkComparison of digital elevation models through the analysis of geomorphic surface remnants in the Desatoya Mountains, Nevada / Bernadett Dobre in Transactions in GIS, vol 25 n° 5 (October 2021)PermalinkImpact of beam diameter and scanning approach on point cloud quality of terrestrial laser scanning in forests / Meinrad Abegg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkLeast squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing / Radhika Ravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkLinear regression and lines intersecting as a method of extracting punctual entities in a lidar point cloud / Marlo Antonio Ribeiro Martins in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])PermalinkPhase unmixing of TerraSAR-X staring spotlight interferograms in building scale for PS height and deformation / Peng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkUrban geospatial information acquisition mobile mapping system based on close-range photogrammetry and IGS site calibration / Ming Guo in Geo-spatial Information Science, vol 24 n° 4 (October 2021)PermalinkMapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)PermalinkCombining photogrammetric and bathymetric data to build a 3D model of a canal tunnel / Emmanuel Moisan in Photogrammetric record, Vol 36 n° 175 (September 2021)PermalinkA comparison of ALS and dense photogrammetric point clouds for individual tree detection in radiata pine plantations / Irfan A. 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Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)PermalinkDouble adaptive intensity-threshold method for uneven Lidar data to extract road markings / Chengming Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkGaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds / Longjie Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkLa modélisation automatique d'un bâtiment à partir d'un nuage de points / Thibault Mauger in XYZ, n° 168 (septembre 2021)PermalinkRelevés de la grotte Cosquer : partie 1, la grotte, les premiers relevés, un monument et une histoire chaotiques / Bertrand Chazaly in XYZ, n° 168 (septembre 2021)PermalinkTarget-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)PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)PermalinkMathematically optimized trajectory for terrestrial close-range photogrammetric 3D reconstruction of forest stands / Karel Kuželka in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkMeasuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)PermalinkScalable surface reconstruction with Delaunay-Graph neural networks / Raphaël Sulzer in Computer graphics forum, vol 40 n° 5 (2021)PermalinkLeaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkDetecting structural changes induced by Heterobasidion root rot on Scots pines using terrestrial laser scanning / Timo P Pitkänen in Forest ecology and management, vol 492 (July-15 2021)PermalinkAn adaptive filtering algorithm of multilevel resolution point cloud / Youyuan Li in Survey review, Vol 53 n° 379 (July 2021)PermalinkExtracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches / Kim Lowell in Marine geodesy, vol 44 n° 4 (July 2021)PermalinkLayout graph model for semantic façade reconstruction using laser point clouds / Hongchao Fan in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkResearch on 3D model reconstruction based on a sequence of cross-sectional images / Zhiguo Dong in Machine Vision and Applications, vol 32 n°4 (July 2021)PermalinkRoad-network-based fast geolocalization / Yongfei Li in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkThree-dimensional reconstruction of single input image based on point cloud / Yu Hou in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)PermalinkVectorized 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)PermalinkIndividual 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)PermalinkRoadside 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)Permalink3D 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)PermalinkForest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 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)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)Permalink