Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras

The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Co...

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Autores: Blanch Gorriz, Xabier, Abellán Fernández, Antonio, Guinau Sellés, Marta
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/157998
Acceso en línea:https://hdl.handle.net/2445/157998
Access Level:acceso abierto
Palabra clave:Lectors òptics
Fotogrametria
Vigilància electrònica
Optical scanners
Photogrammetry
Electronic surveillance
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spelling Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse CamerasBlanch Gorriz, XabierAbellán Fernández, AntonioGuinau Sellés, MartaLectors òpticsFotogrametriaVigilància electrònicaOptical scannersPhotogrammetryElectronic surveillanceThe emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Control Points (GCPs) are not available or the camera system cannot be properly calibrated. This paper presents a new workflow called Point Cloud Stacking (PCStacking) that overcomes these restrictions by making the most of the iterative solutions in both camera position estimation and internal calibration parameters that are obtained during bundle adjustment. The basic principle of the stacking algorithm is straightforward: it computes the median of the Z coordinates of each point for multiple photogrammetric models to give a resulting PC with a greater precision than any of the individual PC. The different models are reconstructed from images taken simultaneously from, at least, five points of view, reducing the systematic errors associated with the photogrammetric reconstruction workflow. The algorithm was tested using both a synthetic point cloud and a real 3D dataset from a rock cliff. The synthetic data were created using mathematical functions that attempt to emulate the photogrammetric models. Real data were obtained by very low-cost photogrammetric systems specially developed for this experiment. Resulting point clouds were improved when applying the algorithm in synthetic and real experiments, e.g., 25th and 75th error percentiles were reduced from 3.2 cm to 1.4 cm in synthetic tests and from 1.5 cm to 0.5 cm in real conditions.MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/157998Articles publicats en revistes (Dinàmica de la Terra i l'Oceà)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.3390/rs12081240Remote Sensing, 2020, vol. 12, num. 8, p. 1240https://doi.org/10.3390/rs12081240cc-by (c) Blanch Gorriz, Xabier et al., 2020http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1579982026-05-27T06:46:51Z
dc.title.none.fl_str_mv Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
title Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
spellingShingle Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
Blanch Gorriz, Xabier
Lectors òptics
Fotogrametria
Vigilància electrònica
Optical scanners
Photogrammetry
Electronic surveillance
title_short Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
title_full Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
title_fullStr Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
title_full_unstemmed Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
title_sort Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
dc.creator.none.fl_str_mv Blanch Gorriz, Xabier
Abellán Fernández, Antonio
Guinau Sellés, Marta
author Blanch Gorriz, Xabier
author_facet Blanch Gorriz, Xabier
Abellán Fernández, Antonio
Guinau Sellés, Marta
author_role author
author2 Abellán Fernández, Antonio
Guinau Sellés, Marta
author2_role author
author
dc.subject.none.fl_str_mv Lectors òptics
Fotogrametria
Vigilància electrònica
Optical scanners
Photogrammetry
Electronic surveillance
topic Lectors òptics
Fotogrametria
Vigilància electrònica
Optical scanners
Photogrammetry
Electronic surveillance
description The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Control Points (GCPs) are not available or the camera system cannot be properly calibrated. This paper presents a new workflow called Point Cloud Stacking (PCStacking) that overcomes these restrictions by making the most of the iterative solutions in both camera position estimation and internal calibration parameters that are obtained during bundle adjustment. The basic principle of the stacking algorithm is straightforward: it computes the median of the Z coordinates of each point for multiple photogrammetric models to give a resulting PC with a greater precision than any of the individual PC. The different models are reconstructed from images taken simultaneously from, at least, five points of view, reducing the systematic errors associated with the photogrammetric reconstruction workflow. The algorithm was tested using both a synthetic point cloud and a real 3D dataset from a rock cliff. The synthetic data were created using mathematical functions that attempt to emulate the photogrammetric models. Real data were obtained by very low-cost photogrammetric systems specially developed for this experiment. Resulting point clouds were improved when applying the algorithm in synthetic and real experiments, e.g., 25th and 75th error percentiles were reduced from 3.2 cm to 1.4 cm in synthetic tests and from 1.5 cm to 0.5 cm in real conditions.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/157998
url https://hdl.handle.net/2445/157998
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/rs12081240
Remote Sensing, 2020, vol. 12, num. 8, p. 1240
https://doi.org/10.3390/rs12081240
dc.rights.none.fl_str_mv cc-by (c) Blanch Gorriz, Xabier et al., 2020
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Blanch Gorriz, Xabier et al., 2020
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Articles publicats en revistes (Dinàmica de la Terra i l'Oceà)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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