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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Main Authors: Xabier Blanch, Antonio Abellan, Marta Guinau
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/12/8/1240
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spelling doaj-a39c96b175a14cc1a57b69ce179f210c2020-11-25T02:53:50ZengMDPI AGRemote Sensing2072-42922020-04-01121240124010.3390/rs12081240Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse CamerasXabier Blanch0Antonio Abellan1Marta Guinau2RISKNAT Research Group, GEOMODELS Research Institute, Faculty of Earth Sciences, Universitat de Barcelona, 08028 Barcelona, SpainInstitute of Applied Geosciences, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UKRISKNAT Research Group, GEOMODELS Research Institute, Faculty of Earth Sciences, Universitat de Barcelona, 08028 Barcelona, SpainThe 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.https://www.mdpi.com/2072-4292/12/8/1240time-lapse photogrammetrymulti-view stereo3D point clouds3D stackingchange detectionrockslope monitoring
collection DOAJ
language English
format Article
sources DOAJ
author Xabier Blanch
Antonio Abellan
Marta Guinau
spellingShingle Xabier Blanch
Antonio Abellan
Marta Guinau
Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
Remote Sensing
time-lapse photogrammetry
multi-view stereo
3D point clouds
3D stacking
change detection
rockslope monitoring
author_facet Xabier Blanch
Antonio Abellan
Marta Guinau
author_sort Xabier Blanch
title Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
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
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2020-04-01
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.
topic time-lapse photogrammetry
multi-view stereo
3D point clouds
3D stacking
change detection
rockslope monitoring
url https://www.mdpi.com/2072-4292/12/8/1240
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