## 5 Results
### Analysis of the Laser Scanning Methods Based on Detail and Density  
The density of a point cloud is one of the key parameters influencing the quality and usability of 3D models, as it directly relates to the resolution and accuracy of the resulting data. Our analysis examined point density along the trajectories of selected scanning systems, enabling a better understanding of the distribution and variability of density depending on the position and scanning conditions. The point density for the individual methods was assessed over an area of 504 m\({}^{2}\). Within this area, the point cloud obtained by the TLS method and Leica RTC360 device contained 22,334,716 points, while the mobile laser scanner Stonex X120 GO produced 3,932,775 points, and the Lidaretto mobile laser scanner recorded 1,231,288 points. Using the TLS method, point density near the scanner's positions reached over 80,000 points/m\({}^{2}\). With the MLS method, the trajectory closely followed building walls and other significant objects. The Stonex X120 GO mobile laser scanner achieved point densities exceeding 20,000 points/m\({}^{2}\) along the trajectory, while the Lidaretto scanner recorded densities of over 3000 points/m\({}^{2}\) (Figure 13). For each surveying method, a histogram based on the point density was created, providing a graphical representation of the point distribution within the point cloud in the same area unit. This approach allows for the identification of areas with higher or lower density and highlights factors that may influence these differences, such as the scanning unit's movement speed, distance from the objects, or obstacles in the terrain. The histogram was generated with CloudCompare software using the Surface Density function, which calculates the number of neighboring points divided by the area of the neighborhood with a radius of 1 cm. Histograms for the datasets from the Leica RTC360, Stonex X120 GO, and Lidaretto are shown in Figure 14, illustrating the general differences in point cloud density among the scanning instruments used. The TLS method achieves an average point density of 54,798 points/m\({}^{2}\), approximately five times higher than the mobile laser scanner Stonex X120 GO, which has an average density of 10,652 points/m\({}^{2}\). The lowest point density is observed with the Lidaretto mobile laser scanner, which has an average density of 2802 points/m\({}^{2}\)--around 20 times less than that of the TLS method. The surveying methods used in our study area were also tested in terms of noise. One of the key parameters in analyzing data obtained from laser scanning is noise, which represents an undesirable impact on the accuracy and quality of point clouds. We tested the noise on the wall of a selected building. In Figure 15, we can observe that the TLS method resulted in the smallest distance between points, contributing to higher data collection accuracy and lower noise levels compared to MLS methods. The average noise value  
Figure 14: Histogram showing the point density in the point clouds obtained by different methods. Figure 13: Density of the points per 1 m\({}^{2}\)—top view of the point clouds. for the TLS method was 0.3 cm. Noise in TLS manifests as random point deviations in the point cloud caused by external factors such as the surface properties of the scanned objects. Compared to the MLS method, where noise levels were higher, TLS showed a significant advantage in that the noise was kept to a minimum in environments with restricted movement and enclosed spaces. Noise in the MLS method mainly arises due to the continuous movement of the scanning device during data collection. The average noise width for the mobile laser scanner Stonex X120 GO reached a value of 2.0 cm. Furthermore, urban environments, which often include various moving objects such as cars, pedestrians, or trees swaying in the wind, can contribute to higher noise levels in the recorded data.