Descripteur
Documents disponibles dans cette catégorie (835)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Range-image: Incorporating sensor topology for lidar point cloud processing / Pierre Biasutti in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)
[article]
Titre : Range-image: Incorporating sensor topology for lidar point cloud processing Type de document : Article/Communication Auteurs : Pierre Biasutti , Auteur ; Jean-François Aujol, Auteur ; Mathieu Brédif , Auteur ; Aurélie Bugeau, Auteur Année de publication : 2018 Projets : GOTMI / Papadakis, Nicolas Article en page(s) : pp 367 - 375 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de partie cachée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] histogramme
[Termes IGN] image 2D
[Termes IGN] objet mobile
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] topologie capteurRésumé : (auteur) This paper proposes a novel methodology for lidar point cloud processing that takes advantage of the implicit topology of various lidar sensors to derive 2D images from the point cloud while bringing spatial structure to each point. The interest of such a methodology is then proved by addressing the problems of segmentation and disocclusion of mobile objects in 3D lidar scenes acquired using street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle those problems directly in the 3D space. This work promotes an alternative approach by using this image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. Using the image derived from the sensor data by exploiting the sensor topology, a semi-automatic segmentation procedure based on depth histograms is presented. Then, a variational image inpainting technique is introduced to reconstruct the areas that are occluded by objects. Experiments and validation on real data prove the effectiveness of this methodology both in terms of accuracy and speed. Numéro de notice : A2018-230 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.6.367 Date de publication en ligne : 01/06/2018 En ligne : https://doi.org/10.14358/PERS.84.6.367 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90171
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 6 (juin 2018) . - pp 367 - 375[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2018061 RAB Revue Centre de documentation En réserve L003 Disponible Documents numériques
en open access
Range-image: Incorporating sensor topology - version HALAdobe Acrobat PDF Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)
[article]
Titre : Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data Type de document : Article/Communication Auteurs : Michalis A. Savelonas, Auteur ; Ioannis Pratikakis, Auteur ; Theoharis Theoharis, Auteur ; Georgios Thanellas, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 1 - 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse spatiale
[Termes IGN] classification barycentrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] codage
[Termes IGN] détection de piéton
[Termes IGN] discrétisation spatiale
[Termes IGN] distribution de Fisher
[Termes IGN] données lidar
[Termes IGN] échantillonnage de données
[Termes IGN] image à basse résolution
[Termes IGN] reconnaissance de formesRésumé : (auteur) Range-based pedestrian recognition is instrumental towards the development of autonomous driving and driving assistance systems. This work introduces encoding methods for pedestrian recognition, based on statistical shape analysis of 3D LIDAR data. The proposed approach has two variants, based on the encoding of local shape descriptors either in a spatially agnostic or spatially sensitive fashion. The latter method derives more detailed cues, by enriching the ‘gross’ information reflected by overall statistics of local shape descriptors, with ‘fine-grained’ information reflected by statistics associated with spatial clusters. Experiments on artificial LIDAR datasets, which include challenging samples, as well as on a large scale dataset of real LIDAR data, lead to the conclusion that both variants of the proposed approach (i) obtain high recognition accuracy, (ii) are robust against low-resolution sampling, (iii) are robust against increasing distance, and (iv) are robust against non-standard shapes and poses. On the other hand, the spatially-sensitive variant is more robust against partial occlusion and bad clustering. Numéro de notice : A2018-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cviu.2018.06.001 Date de publication en ligne : 15/06/2018 En ligne : https://www.sciencedirect.com/science/article/pii/S1077314218300766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92439
in Computer Vision and image understanding > vol 171 (June 2018) . - pp 1 - 9[article]Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology / Stéphane Guinard in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)
[article]
Titre : Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Bruno Vallet , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Papadakis, Nicolas Article en page(s) : pp 63 - 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] complexe simplicial
[Termes IGN] coplanarité
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle géométrique
[Termes IGN] pondération
[Termes IGN] reconstruction 3D
[Termes IGN] relation topologique
[Termes IGN] relation topologique 3D
[Termes IGN] semis de pointsRésumé : (auteur) Nous présentons une nouvelle méthode pour la reconstruction de complexes simpliciaux (ensembles de points, segments et triangles) à partir de nuages de points 3D obtenus par LiDAR mobile, à balayage plan. Notre méthode utilise la topologie inhérente au capteur LiDAR pour définir une relation spatiale entre les points. Pour cela, nous examinons chaque connexion possible entre points, pondérée en fonction de sa distance au capteur, et les filtrons en privilégiant les structures collinéaires, ou perpendiculaires aux impulsions du laser. Ensuite, nous créons et filtrons des triangles pour chaque triplet de segments connectés entre eux, en fonction de leur coplanarité locale. Nous comparons nos résultats à une reconstruction non pondérée d'un complexe simplicial. Numéro de notice : A2018-497 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2018.412 En ligne : https://doi.org/10.52638/rfpt.2018.412 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91263
in Revue Française de Photogrammétrie et de Télédétection > n° 217-218 (juin - septembre 2018) . - pp 63 - 71[article]Sensor-topology based simplicial complex reconstruction from mobile laser scanning / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)
[article]
Titre : Sensor-topology based simplicial complex reconstruction from mobile laser scanning Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Bruno Vallet , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Papadakis, Nicolas Conférence : ISPRS 2018, TC II Mid-term Symposium, Towards Photogrammetry 2020 04/06/2018 07/06/2018 Riva del Garda Italie ISPRS OA Annals Article en page(s) : pp 121 - 128 Note générale : bibliographie
The authors would like to acknowledge the DGA for their financial support of this work.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] reconstruction 3D
[Termes IGN] scène
[Termes IGN] semis de points
[Termes IGN] voisinage (relation topologique)Mots-clés libres : The authors would like to acknowledge the DGA for their financial support of this work Résumé : (auteur) We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create triangles for each triplet of self-connected edges. Last, we improve this method with a regularization based on the co-planarity of triangles and collinearity of remaining edges. We compare our results to a naive simplicial complexes reconstruction based on edge length. Numéro de notice : A2018-271 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-121-2018 Date de publication en ligne : 28/05/2018 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-121-2018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90347
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2 (June 2018) . - pp 121 - 128[article]Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network / Ruibin Zhao in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
[article]
Titre : Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network Type de document : Article/Communication Auteurs : Ruibin Zhao, Auteur ; Mingyong Pang, Auteur ; Jidong Wang, Auteur Année de publication : 2018 Article en page(s) : pp 960 - 979 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods. Numéro de notice : A2018-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1431840 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1431840 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89861
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 960 - 979[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018031 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud / Cigdem Serifoglu Yilmaz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkFrom point cloud to BIM: an integrated workflow for documentation, research and modelling of architectural heritage / C. Rodríguez-Moreno in Survey review, vol 50 n° 360 (May 2018)PermalinkLarge-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)PermalinkLiDAR, a technology to assist with smart cities and climate change resilience: a case study in an urban metropolis / Ryan Garnett in ISPRS International journal of geo-information, vol 7 n° 5 (May 2018)PermalinkRevue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile / Sylvie Daniel in Geomatica, vol 72 n° 1 (March 2018)PermalinkA spatio-temporal index for aerial full waveform laser scanning data / Debra F. Laefer in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkUse of LiDAR for calculating solar irradiance on roofs and façades of buildings at city scale: Methodology, validation, and analysis / Liang Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkUsing terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach / Stéphane Momo Takoudjou in Methods in ecology and evolution, vol 9 n° 4 (April 2018)PermalinkAnalyse du risque végétation dans les emprises ferroviaires à partir de données LiDAR acquises par drones / Luc Perrin in XYZ, n° 154 (mars - mai 2018)PermalinkImportant LiDAR metrics for discriminating forest tree species in Central Europe / Yifang Shi in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkLarge off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkLittoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)PermalinkPredicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkRobust interpolation of DEMs from lidar-derived elevation data / Chuanfa Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkSemantic enrichment of octree structured point clouds for multi‐story 3D pathfinding / Florian W. Fichtner in Transactions in GIS, vol 22 n° 1 (February 2018)PermalinkPermalinkAirborne laser scanning for tree diameter distribution modelling: a comparison of different modelling alternatives in a tropical single-species plantation / Matti Maltamo in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkAssessing forest windthrow damage using single-date, post-event airborne laser scanning data / Gherardo Chirici in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkColorisation of LiDAR point cloud / Mathieu Brédif (2018)PermalinkEstimation cohérente de l'indice de surface foliaire en utilisant des données terrestres et aéroportées / Ronghai Hu (2018)Permalink