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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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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