## 4 Interpretation and Discussion  
### Surveying Small-Scale Morphologies  
In the case of both technologies, a dense point cloud is the final result of the survey. However, while the laser scan data contains information on the intensity of the reflected signal for each measured point, the photogrammetric data provides textural data in the form of RGB values for each reconstructed point. Although the Leica ScanStation C10 scanner is equipped with an integrated digital camera, this camera provides only low quality and resolution of captured images. Therefore, due to the light conditions in the cave, the integrated camera was not used during the laser scanning. Comparison of TLS and DCRP  
In order to compare the final data, point clouds obtained by both technologies were compared by a difference model in CloudCompare 2.10 software using the \"M3C2 distance\" plugin [45], which allows determining robust signed distances directly between two point clouds. Regarding the spatial segmentation, some parts of the Ochtina Aragonite Cave surveyed by both technologies (morphologically contrast wall surfaces represented by inward-inclined smooth solution facets and the adjacent upper part of the wall with asymmetric cusped depressions). Two smaller parts, showing the areas with the highest deviations, were selected for the comparison. The final difference models, together with the frequency distribution graphs of points and their deviations from the normal distribution (Gaussian curve), are shown in Figure 13.  
Figure 13: Difference models between two selected parts of the cave wall surface with graphical analysis of their frequency distribution, and their deviations from the normal distribution. Both difference models reach the mean value of real deviations of about 0 mm (\(-0.1\) mm and \(+0.3\) mm), with standard deviations of 2 mm and 6 mm, respectively, representing a minimal, almost zero, mutual change in size between the compared point clouds. All values of deviations range from \(-8.3\) mm to \(+8.1\) mm (left model), and respectively \(-18.8\) mm to \(+19.0\) mm (right model). Both distributions of points are sharp, which represents a low variability of deviations. The first (left) difference model has only one sharp peak; therefore, also regarding the size of deviations, there is no systematic error. However, the second (right) difference model does not have such a clear course, and there can be found several partial peaks indicating the influence of systematic errors. In both cases, the largest deviations can be found in areas with the greatest curvature of the model, especially at sharp edges, which is also the result of the higher density of point cloud obtained by photogrammetry with respect to laser scanning. Another comparison of both models was performed by two vertical cross-sections with a width of 5 mm through both point clouds (Figure 14). This comparison confirms that the significantly denser point cloud from photogrammetry provides a more detailed and smoother course of the model surface of the selected cave wall than the laser scan data. Additionally, laser scan data points to more significant deviations at sharper edges and transitions, which is also the result of the low density of the scanned data. For the modeling of individual morphological cave structures, the mesh models were created from both point clouds and subsequently compared in CloudCompare 2.10 software by Poisson Surface Reconstruction [46] with Octree Depth 12 (Figure 15). According to this Figure, it is obvious that the mesh model generated from the photogrammetric point cloud provides a significantly more detailed representation of morphological structures (although the visualization of two parts of the 3D model in Figure 10 may indicate that TLS point cloud is dense enough). Figure 14: Two vertical cross-sections through both point clouds with their location. Based on these comparisons, it can be stated that both technologies can be suitably used in underground (cave) surveys for the needs of mapping, morphological analyses, etc.