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Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 343 - 350 Titre : Planar polygons detection in lidar scans based on sensor topology enhanced Ransac Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Zoumana Mallé, Auteur ; Oussama Ennafii , Auteur ; Pascal Monasse, Auteur ; Bruno Vallet , Auteur Année de publication : 2020 Projets : Biom / Vallet, Bruno Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 343 - 350 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] polygone [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] topologie capteur [Termes descripteurs IGN] traitement de nuage de points [Termes descripteurs IGN] transformation de Hough Résumé : (auteur) Detecting planar structures in point clouds is a very central step of the point cloud processing pipeline as many Lidar scans, in particular in anthropic environments, present such planar structures. Many improvements have been proposed to RANSAC and the Hough transform, the two major types of plane detection methods. An important limitation however is that these methods detect planes running across the whole scene instead of more localized planar patches. Moreover, they do not exploit the sensor information that often comes with Lidar point cloud (sensor topology and optical center position in particular). In this paper we address both issues: we aim at detecting planar polygons that have a limited spatial extent, and we exploit sensor topology. The latter is used to enhance a RANSAC framework on two aspects: to make seed points selection more local and to define more compact sets of inliers through sensor space region growing. Numéro de notice : A2020-502 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-343-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-343-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95643 [article]

Provably consistent distributed Delaunay triangulation / Mathieu Brédif in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 195 - 202 Titre : Provably consistent distributed Delaunay triangulation Type de document : Article/Communication Auteurs : Mathieu Brédif , Auteur ; Laurent Caraffa , Auteur ; Murat Yirci, Auteur ; Pooran Memari, Auteur Année de publication : 2020 Projets : IQmulus / Brédif, Mathieu Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 195 - 202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] géomètrie algorithmique [Termes descripteurs IGN] informatique en nuage [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] Spark [Termes descripteurs IGN] traitement de nuage de points [Termes descripteurs IGN] triangulation de Delaunay Résumé : (Auteur) This paper deals with the distributed computation of Delaunay triangulations of massive point sets, mainly motivated by the needs of a scalable out-of-core surface reconstruction workflow from massive urban LIDAR datasets. Such a data often corresponds to a huge point cloud represented through a set of tiles of relatively homogeneous point sizes. This will be the input of our algorithm which will naturally partition this data across multiple processing elements. The distributed computation and communication between processing elements is orchestrated efficiently through an uncentralized model to represent, manage and locally construct the triangulation corresponding to each tile. Initially inspired by the star splaying approach, we review the Tile\& Merge algorithm for computing Distributed Delaunay Triangulations on the cloud, provide a theoretical proof of correctness of this algorithm, and analyse the performance of our Spark implementation in terms of speedup and strong scaling in both synthetic and real use case datasets. A HPC implementation (e.g. using MPI), left for future work, would benefit from its more efficient message passing paradigm but lose the robustness and failure resilience of our Spark approach. Numéro de notice : A2020-410 Affiliation des auteurs : LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-195-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-195-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94979 [article]

Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 50 - 62 Titre : Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification Type de document : Article/Communication Auteurs : Congcong Wen, Auteur ; Lina Yang, Auteur ; Xiang Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 50 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] contrainte de direction [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] traitement de nuage de points Résumé : (auteur) Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. Traditional point cloud classification methods mostly focus on the development of handcrafted point geometry features and employ machine learning-based classification models to conduct point classification. In recent years, the advances of deep learning models have caused researchers to shift their focus towards machine learning-based models, specifically deep neural networks, to classify airborne LiDAR point clouds. These learning-based methods start by transforming the unstructured 3D point sets to regular 2D representations, such as collections of feature images, and then employ a 2D CNN for point classification. Moreover, these methods usually need to calculate additional local geometry features, such as planarity, sphericity and roughness, to make use of the local structural information in the original 3D space. Nonetheless, the 3D to 2D conversion results in information loss. In this paper, we propose a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we design a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method demonstrates superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model achieves a new state-of-the-art performance on powerline, car, and facade categories. Moreover, to demonstrate the generalization abilities of the proposed method, we conduct further experiments on the 2019 Data Fusion Contest Dataset. Our proposed method achieves superior performance than the comparing methods and accomplishes an overall accuracy of 95.6% and an average F1 score of 0.810. Numéro de notice : A2020-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.004 date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94743 [article]

Review of mobile laser scanning target‐free registration methods for urban areas using improved error metrics / Hoang Long Nguyen in Photogrammetric record, vol 34 n° 167 (September 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 167 (September 2019) . - pp 282 – 303 Titre : Review of mobile laser scanning target‐free registration methods for urban areas using improved error metrics Type de document : Article/Communication Auteurs : Hoang Long Nguyen, Auteur ; David Belton, Auteur ; Petra Helmholz, Auteur Année de publication : 2019 Article en page(s) : pp 282 – 303 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme ICP [Termes descripteurs IGN] appariement de points [Termes descripteurs IGN] erreur de mesure [Termes descripteurs IGN] qualité du processus [Termes descripteurs IGN] traitement de nuage de points [Termes descripteurs IGN] zone urbaine Mots-clés libres : The aim of the registration process is to obtain the transformation parameters to transform captured point clouds to their correct positions. Résumé : (auteur) Registration is one of the most important tasks in mobile laser scanning (MLS) point cloud processing. This paper firstly reviews existing target‐free matching techniques as well as methods to evaluate the quality of the registration. Next, a new error metric is introduced that takes into account the residuals of check planes as well as their orientation. Experiments using real datasets in combination with reference data were performed to evaluate the suitability of these metrics. The proposed error metric proved to be more suitable for evaluating the quality of point cloud registration than state‐of‐the‐art equivalents. The results also indicate that least squares plane fitting is the best technique for MLS point cloud registration. Numéro de notice : A2019-498 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12293 date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1111/phor.12293 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93760 [article]

Relevé de la grotte glacée de Cenote Abyss dans les Dolomites / Farouk Kadded in XYZ, n° 157 (décembre 2018 - février 2019)

Public [article]  inXYZ > n° 157 (décembre 2018 - février 2019) . - pp 57 - 60 Titre : Relevé de la grotte glacée de Cenote Abyss dans les Dolomites Type de document : Article/Communication Auteurs : Farouk Kadded, Auteur ; Tommasso Santagata, Auteur Année de publication : 2018 Article en page(s) : pp 57 - 60 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] 3DReshaper [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] glace [Termes descripteurs IGN] grotte [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] instrumentation Leica [Termes descripteurs IGN] Italie [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] spéléologie [Termes descripteurs IGN] traitement de nuage de points Résumé : (auteur) The abyss of Conturines, alsoknown as Cenote Abyss, represents one of the deepest and lagest caves of the Dolomites.This 280 m deep cave is characterized by a huge ice deposit, which makes it of major interest for the study of paleoclimate and modern climate change in this region of the Alps.The cave was discovered in 1994 after the abrupt empying of a lake at 2940 m. In 2015, a research project began to monitor the long-term movements an d volume changes of this ice deposit and to understand the microclimate of the cave. With two expeditions organized in October 2015 and September 2016, a complete study of the cave was carried out using a 3D laser scanner HDS7000 in 2015 and a scanner ScanStation P40 in 2016. Installation of barometric datalodgers, temperature and humidity in different areas of the cave provide a one-year record of the microclimate. In addtion, pollen traps have been installed to study the current flow of pollen on and inside the cave while preliminary analyzes of pollen grains held in the ice are in progress. The Cenote Abyss Ice Caves Research Project aim to shed light on the changing climate of the Dolomites over the past several hundred years, perhaps thousands of years, as well as on environmental changes more recent events that led to the melting of the cave glacier. Numéro de notice : A2018-546 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91582 [article]

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