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

Xabier Blanch,Antonio Abellan,Marta Guinau

doi:10.3390/rs12081240

Abstract

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.

Highlights

The acquisition of point clouds (PCs) using photogrammetric techniques for three-dimensional (3D) modelling of natural surfaces has increased significantly in recent years [1,2]
The histograms of the differences between the Enh-PCn and the Reference PC (Ref-PC) (Figure 7a) provide a quantitative assessment of the improvement achieved with the PCStacking algorithm
The comparisons between Enh-PCn vs. Ref-PC in the redundancy test (Figure 8a) reveal a considerable reduction of the standard deviation when increasing the number of models introduced

Summary

Introduction

The acquisition of point clouds (PCs) using photogrammetric techniques for three-dimensional (3D) modelling of natural surfaces has increased significantly in recent years [1,2]. Kromer et al [11] and Lague et al [14] obtained a better level of detection on PC comparison by minimizing point scattering around a central value, they do not improve the PC per se. These algorithms were originally developed with the aim of improving results by taking into account the properties and errors of LiDAR datasets, they can be used with PCs captured using different sensors (LiDAR, sonar, etc.); the particular characteristics of photogrammetric data such as non-linear and time-variant errors require the development of new methodologies designed to overcome the constraints of this technique. Improving PC quality by stacking multiple low-quality datasets as those generated using time-lapse cameras has not been explored yet

Methods

Results

Discussion

Conclusion