# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 6 A Real Case Study: The Bossea Cave
### Analysis of the Results  
The geometric quality of the entire point cloud was estimated using as reference data a terrestrial laser scanner (TLS) survey of the entire cave, which was realized for a previous project.  
The used instrument was the FARO Focus 3D. It is a well-known phase-shift laser scanner with a distance accuracy of up to \(\pm 0.002\) m and a range from 0.6 m up to 120 m. For the registration of the LiDAR point clouds, some markers, spread over the area, were measured with topographic instruments. As usual, first a reference network was realized with Global Navigation Satellite System (GNSS) techniques to know high-precision vertices. The survey of the two vertices was made through a GNSS double frequency and multi-constellation receiver in static modality standing on each point for about 60 min. Afterwards, the total station was employed to realize a network of points and to measure  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Images** & **Aligned Images** & **Points Dense Point Cloud** & **Computational Time** \\ \hline Wall 1 & 138 & 138 & 7,948,534 & 47 min \\ Wall 2 & 106 & 106 & 8,872,074 & 32 min \\ Wall 3 & 90 & 90 & 5,688,908 & 23 min \\ \hline \hline \end{tabular}
\end{table}
Table 11: The characteristics of the dense point clouds obtained with AMP from the smartphone images of the surfaces of the cave.  
Figure 8: The resulting dense point clouds.  
the position of the markers. All measurements were subsequently adjusted with the MicroSurvey StarNet v.7.0 software, in order to obtain the final coordinates, which were determined with a millimeter accuracy. The final model was obtained with the FARO Scene software, with a registration error of about 1 cm, compatible with the accuracy of the topographic network and the instrument itself.  
The photogrammetric models were subsequently compared with the LiDAR point cloud using the software CloudCompare. To this purpose, it was necessary to scale the model and rototranslate it, so that it was placed in a reference system compatible with that of the reference model. Therefore, the ICP algorithm, implemented in the CloudCompare software, was used again. Subsequently, it was possible to estimate the Euclidean distances between the points of the two representations.  
Figure 9 and Table 12 show some significant results deriving from this procedure in histograms and in statistical distributions. The computation of the differences confirmed the centimetric accuracy of the models generated by AMP, and showed that the surfaces have larger discrepancies where there are holes in the LiDAR point cloud. Other errors can be attributed to the noise affecting the point clouds, due to the low light and large shaded areas, as well as to the reflective surfaces resulting from high humidity. In order to improve the process of bundle block adjustment and facilitate the correct alignment of the images, some topographic measured markers should be used. Furthermore, some of the same markers could be employed as check points for a further evaluation of the accuracy of the dense reconstruction.