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Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation / Zhimin Yuan in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 61 n° 1 (January 2023) . - n° 5700613 Titre : Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation Type de document : Article/Communication Auteurs : Zhimin Yuan, Auteur ; Ming Cheng, Auteur ; Wankang Zeng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5700613 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] alignement des données [Termes IGN] apprentissage non-dirigé [Termes IGN] compression de données [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Second, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: 1) build an auxiliary nonparametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers and 2) introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets. Numéro de notice : A2023-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3234542 Date de publication en ligne : 05/01/2023 En ligne : https://doi.org/10.1109/TGRS.2023.3234542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102489 [article]

Deep learning-based point cloud compression / Maurice Quach (2022)  

Public Titre : Deep learning-based point cloud compression Titre original : Compression de nuages de points par apprentissage profond Type de document : Thèse/HDR Auteurs : Maurice Quach, Auteur ; Frédéric Dufaux, Directeur de thèse ; Giuseppe Valenzise, Directeur de thèse Editeur : Bures-sur-Yvette : Université Paris-Saclay Année de publication : 2022 Importance : 165 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de Doctorat de l'Université de Saclay, spécialité Traitement du signal et des images Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] attribut [Termes IGN] compression d'image [Termes IGN] compression de données [Termes IGN] géométrie [Termes IGN] semis de points [Termes IGN] stockage de données Index. décimale : THESE Thèses et HDR Résumé : (auteur) Point clouds are becoming essential in key applications with advances in capture technologies leading to large volumes of data.Compression is thus essential for storage and transmission.Point Cloud Compression can be divided into two parts: geometry and attribute compression.In addition, point cloud quality assessment is necessary in order to evaluate point cloud compression methods.Geometry compression, attribute compression and quality assessment form the three main parts of this dissertation.The common challenge across these three problems is the sparsity and irregularity of point clouds.Indeed, while other modalities such as images lie on a regular grid, point cloud geometry can be considered as a sparse binary signal over 3D space and attributes are defined on the geometry which can be both sparse and irregular.First, the state of the art for geometry and attribute compression methods with a focus on deep learning based approaches is reviewed.The challenges faced when compressing geometry and attributes are considered, with an analysis of the current approaches to address them, their limitations and the relations between deep learning and traditional ones.We present our work on geometry compression: a convolutional lossy geometry compression approach with a study on the key performance factors of such methods and a generative model for lossless geometry compression with a multiscale variant addressing its complexity issues.Then, we present a folding-based approach for attribute compression that learns a mapping from the point cloud to a 2D grid in order to reduce point cloud attribute compression to an image compression problem.Furthermore, we propose a differentiable deep perceptual quality metric that can be used to train lossy point cloud geometry compression networks while being well correlated with perceived visual quality and a convolutional neural network for point cloud quality assessment based on a patch extraction approach.Finally, we conclude the dissertation and discuss open questions in point cloud compression, existing solutions and perspectives. We highlight the link between existing point cloud compression research and research problems to relevant areas of adjacent fields, such as rendering in computer graphics, mesh compression and point cloud quality assessment. Note de contenu : 1- Introduction 2- State of the Art on point cloud compression 3- Convolutional neural networks for lossy PCGC 4- Deep generative model for lossless PCGC 5- Deep multiscale lossless PCGC 6- Folding-based PCAC 7- Deep perceptual point cloud quality metric 8- Convolutional Neural Network for PCQA Numéro de notice : 24081 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de doctorat : Traitement du signal et des images : Paris-Saclay : 2022 Organisme de stage : Laboratoire des signaux et systèmes DOI : sans En ligne : https://theses.hal.science/tel-03894261 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102331

Road network simplification for location-based services / Abdeltawab M. Hendawi in Geoinformatica, vol 24 n° 4 (October 2020)  

Public [article]  inGeoinformatica > vol 24 n° 4 (October 2020) . - pp 801 - 826 Titre : Road network simplification for location-based services Type de document : Article/Communication Auteurs : Abdeltawab M. Hendawi, Auteur ; John A. Stankovic, Auteur ; Ayman Taha, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 801 - 826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] appariement de cartes [Termes IGN] appariement de données localisées [Termes IGN] appariement de graphes [Termes IGN] carte routière [Termes IGN] compression de données [Termes IGN] modèle de Markov caché [Termes IGN] réseau routier [Termes IGN] service fondé sur la position [Termes IGN] simplification de contour [Termes IGN] stockage de données [Vedettes matières IGN] Généralisation Résumé : (Auteur) Road-network data compression or simplification reduces the size of the network to occupy less storage with the aim to fit small form-factor routing devices, mobile devices, or embedded systems. Simplification (a) reduces the storage cost of memory and disks, and (b) reduces the I/O and communication overhead. There are several road network compression techniques proposed in the literature. These techniques are evaluated by their compression ratios. However, none of these techniques takes into consideration the possibility that the generated compressed data can be used directly in Map-matching operation which is an essential component for all location-aware services. Map-matching matches a measured latitude and longitude of an object to an edge in the road network graph. In this paper, we propose a novel simplification technique, named COMA, that (1) significantly reduces the size of a given road network graph, (2) achieves high map-matching quality on the simplified graph, and (3) enables the generated compressed road network graph to be used directly in map-matching and location-based applications without a need to decompress it beforehand. COMA smartly deletes those nodes and edges that will not affect the graph connectivity nor causing much of ambiguity in the map-matching of objects’ location. COMA employs a controllable parameter; termed a conflict factor C, whereby location aware services can trade the compression gain with map-matching accuracy at varying granularity. We show that the time complexity of our COMA algorithm is O(|N|log|N|). Intensive experimental evaluation based on a real implementation and data demonstrates that COMA can achieve about a 75% compression-ratio while preserving high map-matching quality. Road Network, Simplification, Compression, Spatial, Location, Performance, Accuracy, Efficiency, Scalability. Numéro de notice : A2020-495 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00406-x Date de publication en ligne : 01/05/2020 En ligne : https://doi.org/10.1007/s10707-020-00406-x Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96115 [article]

Hub Labels on the database for large-scale graphs with the COLD framework / Alexandros Efentakis in Geoinformatica, vol 21 n° 4 (October - December 2017)  

Public [article]  inGeoinformatica > vol 21 n° 4 (October - December 2017) . - pp 703 - 732 Titre : Hub Labels on the database for large-scale graphs with the COLD framework Type de document : Article/Communication Auteurs : Alexandros Efentakis, Auteur ; Christodoulos Efstathiades, Auteur ; Dieter Pfoser, Auteur Année de publication : 2017 Article en page(s) : pp 703 - 732 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] base de données localisées [Termes IGN] chemin le plus court, algorithme du [Termes IGN] compression de données [Termes IGN] graphe [Termes IGN] plus proche voisin, algorithme du [Termes IGN] SQL Résumé : (Auteur) Shortest-path computation on graphs is one of the most well-studied problems in algorithmic theory. An aspect that has only recently attracted attention is the use of databases in combination with graph algorithms, so-called distance oracles, to compute shortest-path queries on large graphs. To this purpose, we propose a novel, efficient, pure-SQL framework for answering exact distance queries on large-scale graphs, implemented entirely on an open-source database engine. Our COLD framework (COmpressed Labels on the Database) can answer multiple distance queries (vertex-to-vertex, one-to-many, k-Nearest Neighbors, Reverse k-Nearest Neighbors, Reverse k-Farthest Neighbors and Top-k Range) not handled by previous methods, rendering it a complete database solution for a variety of practical large-scale graph applications. Our experimentation shows that COLD outperforms existing approaches (including popular graph databases) in terms of query time and efficiency, while requiring significantly less storage space than these methods. Numéro de notice : A2017-601 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0287-5 En ligne : https://doi.org/10.1007/s10707-016-0287-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86909 [article]

Traitement des nuages de points sous PostGIS / Ludovic Delauné in Géomatique expert, n° 113 (novembre - décembre 2016)

Public [article]  inGéomatique expert > n° 113 (novembre - décembre 2016) . - pp 19 - 20 Titre : Traitement des nuages de points sous PostGIS Type de document : Article/Communication Auteurs : Ludovic Delauné, Auteur Année de publication : 2016 Article en page(s) : pp 19 - 20 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] compression de données [Termes IGN] données lidar [Termes IGN] format LAS [Termes IGN] PostGIS [Termes IGN] semis de points [Termes IGN] stockage de données Résumé : (Auteur) [Introduction] Les nuages de points issus des survols laser sont une description remarquablement précise de notre environnement, à partir de laquelle il est possible de dériver un nombre important de données. Cette richesse est en partie liée à leur densité : un équipement de type LIDAR relève jusqu'à deux millions de points par seconde. Cela signifie sept milliards par heure, ces points étant caractérisés par des coordonnées d'espace (x, y, z) plus - accessoirement - des informations de couleurs et/ou de réflectance. Tout cela donne des fichiers qui peuvent facilement atteindre plusieurs téra-octets. D'où la question : comment les stocker pour les exploiter efficacement ? [...] Numéro de notice : A2016-960 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83617 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
265-2016061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
IFN-001-P001918	PER	Revue	Nogent-sur-Vernisson	Salle périodiques	Exclu du prêt

SPLZ: An efficient algorithm for single source shortest path problem using compression method / Jingwei Sun in Geoinformatica, vol 20 n° 1 (January - March 2016)  

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Point cloud server (PCS) : point clouds in-base management and processing / Rémi Cura in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W5 (October 2015)  

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Compression strategies for LiDAR waveform cube / Grzegorz Jóźków in ISPRS Journal of photogrammetry and remote sensing, vol 99 (January 2015)  

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Compression of trajectory data: a comprehensive evaluation and new approach / Jonathan Muckell in Geoinformatica, vol 18 n° 3 (July 2014)  

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One billion points in the cloud – an octree for efficient processing of 3D laser scans / Jan Elseberg in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)  

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Théorie des codes : compression, cryptage, correction / J. G. Dumas (2013)

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Variable-resolution compression of vector data / B. Yang in Geoinformatica, vol 12 n° 3 (September - November 2008)  

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A compression format and tools for GNSS observation data / Yuki Hatanaka in Bulletin of the Geographical survey institute, vol 55 (March 2008)  

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Le traitement du signal sous Matlab / André Quinquis (2007)

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Data compression and OGC standards: "GML becoming the geospatial language of the web" / S. Bacharach in Geoinformatics, vol 9 n° 8 (01/12/2006)

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