An Introduction to Fully Automated Co-Alignment - FACA

Well-aligned point cloud time series data generated with Unmanned Aerial Vehicles (UAVs) can be a significant asset to geoscientists.
Practitioners benefit from multi-temporal point clouds with high comparative accuracy, e.g. to evaluate landscape changes after landslides and quantify mass wasting.
Two approaches are usually applied to achieve the accurate alignment of point clouds: indirect and direct georeferencing.
Indirect georeferencing uses well distributed Ground Control Points (GCPs) in the study area.
While this method significantly enhances the precision and accuracy of time series point clouds, the placement and measurement of GCPs are time-intensive and may even be impossible in difficult terrain.
Direct georeferencing depends on highly precise and accurate location information embedded in images, which is often viable only with expensive real-time kinematic (RTK) positioning equipment or post-processed kinematic (PPK) services.
Beyond the extra cost, this approach faces the same challenges as indirect georeferencing, particularly in the placement of equipment and scalability for large areas.

Recent research has introduced an alternative method called Co-Alignment, which enables the alignment of point clouds with high local precision without GCPs and RTK data. Moreover, when GCPs or RTK are used, co-alignment can further enhance accuracy of the point cloud alignment.
This method aligns multiple point clouds with good local precision without requiring GCPs or RTK equipment, though it lacks global accuracy.
The workflow uses common, unchanged features in the study area, such as anthropogenic structures or boulders, to establish spatial references across multiple epochs using computer vision algorithms.

We developed FACA - Fully Automated Co-Alignment to implement the Co-Alignment workflow.
With FACA, we aim to offer easy access to a scalable point cloud alignment method.
FACA is automatable from the command line and user-friendly through a custom graphical user interface, making it adaptable to common point cloud generation workflows.
Released as open-source software under the GNU General Public License v3, FACA is freely accessible and modifiable to meet diverse user requirements.
By integrating with Agisoft Metashape Professional, FACA leverages advanced photogrammetric features to enhance performance and output quality.
We present the FACA workflow, emphasizing its ease of use, scalability, performance, supported by results from data acquired at Germany's Baltic Sea coast and in Svalbard.

Furthermore, we discuss the potential for custom software solutions to further improve and expand the workflow’s capabilities.