## 4 Discussion  
Scans performed under controlled laboratory conditions on materials in the group of plasters, plastics and frosted glass reported generally excellent SF\({}_{\lambda}\) and P\({}_{\lambda}\) values. This is a positive aspect considering that LiDAR smartphones, in their working conditions, may have to scan different material surfaces in the same scenario (e.g., a building, a square, a street, etc.). Observing the SF\({}_{\lambda}\) values in detail, there is a division between the \"plasters\" group and the \"plastics and frosted glass\" group, which have lower values than \"plasters\". The difference is very small, and we estimate that this is not a problem for multimaterial scanning. The steel and brass samples scanned at a distance of 0.5 m and resolutions of 1 cm and 1.5 cm showed considerable noise to the extent that they could not be analysed. Scanning the same samples at a resolution of 2 cm showed a surface variation value of 0.1672 and a cracked and deformed surface (Figure 15a). Steel and brass samples scanned at a distance of 1.5 m showed generally better surface variation values than scans performed at a closer distance (0.5 m). The 1 cm resolution scan of brass was good, while the 1.5 cm and 2 cm resolution scans produced surface variation values of 0.1455 and 0.1000 due to some splitting and deformation (Figure 15b,c). Problems in scanning metallic materials could be caused by the surface structure; metallic materials can be scanned more easily by increasing the acquisition distance and angle of the laser beam [15]. The planarity values for the Android device are interesting, as this device achieved equal or better results than iOS devices with a lower resolution. The possible deformations of planar surfaces can be better evaluated only in tests performed under dynamic conditions. Analyses regarding a linear correlation between albedo and surface variation showed a moderate to very weak relationship. This is another positive element since it indicates that the nature of the material does not primarily affect the quality of the scans. Through tests performed under dynamic conditions, we were able to highlight the problems associated with moving scans. Two main problems emerged: the splitting of surfaces and the incorrect closure of scans caused by inertial navigation system (INS) drift problems. Drift problems depend on calculation of the smartphone's position. During the scanning process, the position of the smartphone is determined instant by instant based on the previous position estimated by inertial sensors. In this process, small errors of the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Object Scanned**} & \multicolumn{2}{c}{**Resolution: 1 cm**} & \multicolumn{2}{c}{**Resolution: 1.5 cm**} & \multicolumn{2}{c}{**Resolution: 2 cm**} \\ \cline{2-7}  & \(\mu\)**C2C [m]** & \(\sigma\)**C2C [m]** & \(\mu\)**C2C [m]** & \(\sigma\)**C2C [m]** & \(\mu\)**C2C [m]** & \(\sigma\)**C2C [m]** \\ \hline Laboratory room & 0.0334 & 0.0264 & 0.0224 & 0.0449 & 0.0518 & 0.0580 \\ Doric column rests & 0.0153 & 0.0132 & 0.0383 & 0.0238 & 0.0127 & 0.0107 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean and standard deviation values of C2C distance obtained in the third and fourth case studies. Figure 15: Scans affected by aberrations; scalar field refers to the surface variation value: steel with 2 cm resolution (**a**), brass with 1.5 cm resolution (**b**), brass with 2 cm resolution (**c**). INS can propagate and cause large positioning errors in the scanning of large objects or large scenarios [13; 25]. However, the drift problems in practice also resulted in the surfaces splitting and overlapping in the scan closure areas. Therefore, it can also be said that the two problems are, to a certain degree, related. Observing the second case study, it can be deduced that increasing the distance to the object being scanned increases the possibility of encountering drift problems.