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Point cloud registration for LiDAR and photogrammetric data: A critical synthesis and performance analysis on classic and deep learning algorithms / Ningli Xu in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)
[article]
Titre : Point cloud registration for LiDAR and photogrammetric data: A critical synthesis and performance analysis on classic and deep learning algorithms Type de document : Article/Communication Auteurs : Ningli Xu, Auteur ; Rongjun Qin, Auteur ; Shuang Song, Auteur Année de publication : 2023 Article en page(s) : n° 100032 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] chevauchement
[Termes IGN] données lidar
[Termes IGN] processus gaussien
[Termes IGN] recalage de données localisées
[Termes IGN] semis de points
[Termes IGN] superposition de donnéesRésumé : (auteur) Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, and application, therefore the current practices in various point cloud registration tasks are still ad-hoc processes. Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic hand-crafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and provide our recommendations, and outlook future efforts. Numéro de notice : A2023-149 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2023.100032 Date de publication en ligne : 16/02/2023 En ligne : https://doi.org/10.1016/j.ophoto.2023.100032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102808
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 8 (April 2023) . - n° 100032[article]A hierarchical multiview registration framework of TLS point clouds based on loop constraint / Hao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)
[article]
Titre : A hierarchical multiview registration framework of TLS point clouds based on loop constraint Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Li Yan, Auteur ; Hong Xie, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 65 - 76 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] appariement de points
[Termes IGN] approche hiérarchique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] recalage d'image
[Termes IGN] semis de points
[Termes IGN] superposition de données
[Termes IGN] traitement de semis de pointsRésumé : (auteur) Automatic registration of multiple point clouds is a significant preprocessing step for 3D computer vision tasks including semantic segmentation, 3D modelling, change detection, etc. Many methods were proposed to deal with this problem and yet most of them are not fully utilizing the redundant information offered by multiple common overlaps among point clouds. The existing methods are also inefficient when dealing with large-scale point clouds. In this paper, a novel automatic registration framework is presented to align point clouds efficiently and robustly. First, the overall number of scans is grouped into several scan-blocks by a proposed blocking strategy, and we build the pairwise relationship among scans through a fully connected graph in each scan-block. Second, perform loop-based coarse registration in each scan-block using a proposed false matches removal strategy. The proposed strategy can effectively identify grossly wrong scan-to-scan matches. Third, the minimum spanning tree is extracted from the graph, and ICP is applied along its edges. Moreover, the Lu–Milios algorithm is used to further optimize all poses at once by utilizing all redundant information in each scan-block. Finally, global block-to-block registration aligns all scan-blocks into a uniform coordinate reference. We test our framework on challenging WHU-TLS datasets, ETH datasets, and Robotic 3D Scan datasets to evaluate the efficiency, accuracy, as well as robustness. The experiment results show that our method achieves the state-of-the-art accuracy, while the time performance is improved by more than 30% compared with the state-of-the-art algorithms. Our source code is made available at https://github.com/WuHao-WHU/HL-MRF for benchmarking purposes. Numéro de notice : A2023-008 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.11.004 Date de publication en ligne : 19/11/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.11.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102112
in ISPRS Journal of photogrammetry and remote sensing > vol 195 (January 2023) . - pp 65 - 76[article]Automatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)
[article]
Titre : Automatic registration method of multi-source point clouds based on building facades matching in urban scenes Type de document : Article/Communication Auteurs : Yumin Tan, Auteur ; Yanzhe Shi, Auteur ; Yunxin Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 767 - 782 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] appariement de formes
[Termes IGN] appariement de points
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] façade
[Termes IGN] fusion de données multisource
[Termes IGN] modélisation 3D
[Termes IGN] photogrammétrie aérienne
[Termes IGN] points registration
[Termes IGN] Ransac (algorithme)
[Termes IGN] recalage de données localisées
[Termes IGN] scène urbaine
[Termes IGN] superposition de donnéesRésumé : (auteur) Both UAV photogrammetry and lidar have become common in deriv- ing three-dimensional models of urban scenes, and each has its own advantages and disadvantages. However, the fusion of these multisource data is still challenging, in which registration is one of the most important stages. In this paper, we propose a method of coarse point cloud registration which consists of two steps. The first step is to extract urban building facades in both an oblique photogrammetric point cloud and a lidar point cloud. The second step is to align the two point clouds using the extracted building facades. Object Vicinity Distribution Feature (Dijkman and Van Den Heuvel 2002) is introduced to describe the distribution of building facades and register the two heterologous point clouds. This method provides a good initial state for later refined registration process and is translation, rotation, and scale invariant. Experiment results show that the accuracy of this proposed automatic registration method is equiva- lent to the accuracy of manual registration with control points. Numéro de notice : A2022-882 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00069R3 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.14358/PERS.22-00069R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102206
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 12 (December 2022) . - pp 767 - 782[article]A unified framework for automated registration of point clouds, mesh surfaces and 3D models by using planar surfaces / Yuan Zhao in Photogrammetric record, vol 37 n° 180 (December 2022)
[article]
Titre : A unified framework for automated registration of point clouds, mesh surfaces and 3D models by using planar surfaces Type de document : Article/Communication Auteurs : Yuan Zhao, Auteur ; Hang Zhao, Auteur ; Marko Radanovic, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 366 - 384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] chevauchement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] maillage
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] recalage de données localisées
[Termes IGN] semis de points
[Termes IGN] superposition de données
[Termes IGN] surface planeRésumé : (auteur) Numéro de notice : A2022-939 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12428 Date de publication en ligne : 18/10/2022 En ligne : https://doi.org/10.1111/phor.12428 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102685
in Photogrammetric record > vol 37 n° 180 (December 2022) . - pp 366 - 384[article]Summarizing large scale 3D mesh for urban navigation / Imeen Ben Salah in Robotics and autonomous systems, vol 152 (June 2022)
[article]
Titre : Summarizing large scale 3D mesh for urban navigation Type de document : Article/Communication Auteurs : Imeen Ben Salah, Auteur ; Sébastien Kramm, Auteur ; Cédric Demonceaux, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104037 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme ICP
[Termes IGN] carte en 3D
[Termes IGN] données lidar
[Termes IGN] entropie
[Termes IGN] image hémisphérique
[Termes IGN] image RVB
[Termes IGN] information sémantique
[Termes IGN] localisation basée vision
[Termes IGN] maillage
[Termes IGN] navigation autonome
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] précision radiométrique
[Termes IGN] profondeur
[Termes IGN] Rouen
[Termes IGN] saillance
[Termes IGN] zone urbaineRésumé : (auteur) Cameras have become increasingly common in vehicles, smartphones, and advanced driver assistance systems. The areas of application of these cameras in the world of intelligent transportation systems are becoming more and more varied: pedestrian detection, line crossing detection, navigation, …A major area of research currently focuses on mapping that is essential for localization and navigation. However, this step generates an important problem of memory management. Indeed, the memory space required to accommodate the map of a small city is measured in tens gigabytes. In addition, several providers today are competing to produce High-Definition (HD) maps. These maps offer a rich and detailed representation of the environment for highly accurate localization. However, they require a large storage capacity and high transmission and update costs. To overcome these problems, we propose a solution to summarize this type of map by reducing the size while maintaining the relevance of the data for navigation based on vision only. The summary consists in a set of spherical images augmented by depth and semantic information and allowing to keep the same level of visibility in every directions. These spheres are used as landmarks to offer guidance information to a distant agent. They then have to guarantee, at a lower cost, a good level of precision and speed during navigation. Some experiments on real data demonstrate the feasibility for obtaining a summarized map while maintaining a localization with interesting performances. Numéro de notice : A2022-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.robot.2022.104037 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1016/j.robot.2022.104037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100335
in Robotics and autonomous systems > vol 152 (June 2022) . - n° 104037[article]Decision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)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)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)PermalinkAutomated registration of SfM‐MVS multitemporal datasets using terrestrial and oblique aerial images / Luigi Parente in Photogrammetric record, vol 36 n° 173 (March 2021)PermalinkImproving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkCartographie dense et compacte par vision RGB-D pour la navigation d’un robot mobile / Bruce Canovas (2021)PermalinkUnderstanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkAn integrated approach to registration and fusion of hyperspectral and multispectral images / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkDelineating minor landslide displacements using GPS and terrestrial laser scanning-derived terrain surfaces and trees: a case study of the Slumgullion landslide, Lake City, Colorado / Jin Wang in Survey review, vol 52 n° 372 (May 2020)PermalinkRestitution de profils verticaux de la distribution de gouttes de pluie à partir de mesures au sol et en altitude / Christophe Samboun (2020)PermalinkUnsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkAutomated fusion of forest airborne and terrestrial point clouds through canopy density analysis / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkReview 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)PermalinkFusion de sets de photos provenant de capteurs différents dans le domaine de l’archéologie / Hugo De Paulis (2019)PermalinkPermalinkRecalage conjoint de données de cartographie mobile et de modèles 3D de bâtiments / Miloud Mezian (2019)PermalinkVision-based localization with discriminative features from heterogeneous visual data / Nathan Piasco (2019)PermalinkAn integrated framework for the spatio–temporal–spectral fusion of remote sensing images / Huanfeng Shen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkAutomatic registration of MLS point clouds and SfM meshes of urban area / Reiji Yoshimura in Geo-spatial Information Science, vol 19 n° 3 (October 2016)PermalinkA Computationally efficient algorithm for fusing multispectral and hyperspectral images / Raúl Guerra in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkExploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkAutonomous navigation in complex nonplanar environments based on laser ranging / Philipp Andreas Krüsi (2016)PermalinkRegistration of aerial imagery and lidar data in desert areas using sand ridges / Na Li in Photogrammetric record, vol 30 n° 151 (September - November 2015)PermalinkAutomatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models / Bernard O. Abayowa in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)PermalinkA critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkA GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation / Xiran Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkRegistration of terrestrial mobile laser data on 2D or 3D geographic database by use of a non-rigid ICP approach / Fabrice Monnier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-5 W2 (November 2013)Permalink3D range scan enhancement using image-based methods / Steffen Herbort in ISPRS Journal of photogrammetry and remote sensing, vol 84 (October 2013)PermalinkTowards 3D lidar point cloud registration improvement using optimal neighborhood knowledge / Adrien Gressin in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkMise en cohérence de données laser cartographique par recalage non-rigide / Fabrice Monnier in Revue Française de Photogrammétrie et de Télédétection, n° 202 (Avril 2013)PermalinkCalibration extrinsèque d'un scanner laser multi-fibre / Anthony Wiart (2013)PermalinkOn the detection of systematic errors in terrestrial laser scanning data / J. 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