## 6 Integration of Terrestrial Scanning, Terrestrial Photogrammetry and Aerial Photogrammetry  
The next processing step was the integration of point clouds from both 3D laser scanning, ground scanning and aerial photogrammetry. Point cloud outputs from the laser scanner contained so-called dead spots. This means that the model was not complete, since the scanning laser beam did not receive all the information for each location in the object during the scanning process. This was caused by both the angle and the position of the scanner. However, these dead spots can be supplemented with information from ground and aerial photogrammetry. Based on this fact, there were two cloud points that needed to be combined. Exported point clouds data were imported into the open source CloudCompare environment, where the process of cleaning and alignment was performed. During the importing, the position of data was adjusted to the local coordinate system with smaller coordinates [43]. Trimming of point clouds of gross errors was performed by the segmentation function. The tool used allowed us to choose segmentation of individual entities through polygons or rectangles. If the rotation was used, it was necessary to define the polygon, since it is available only for 2D. Individual entities were represented by surrounding vegetation, fencing, mechanisms (in photogrammetry) and paved areas. Subsequently, the statistical outlier removal (SOR) noise filter was applied to each point cloud [44]. The SOR algorithm calculates the average distance to each nearest neighbor point selected. It evaluates as the noise points those points that are further than the average distance and n-times the standard deviation. In this case, the basic settings defined by the software were sufficient. Segmented point clouds could then be combined (interconnected). Figure 9: Point cloud of the Kunerad manor. The reason for this was the difference in the coordinate systems of the software from which data were originally exported. CloudCompare has several ways to align boundaries, including bounding box alignment, manual point-to-point transformation and automatic alignment. Mutual points between laser scanning and photogrammetry had already been defined, so we applied alignment by using these points. To precisely specify individual common points, it is advisable that each of them carries an RGB value for each point, which will facilitate the search for points. The result of the alignment of the individual point clouds is shown in Figure 10. The histogram shows that the largest part consisted of the interconnection points with the smallest distance. The increasing distance was mostly formed by data from photogrammetry, where the laser device was unable to digitize the object. The created point cloud was exported in the structured E57 format and returned to the PR3 DM generation process. The exported point cloud was reimported into the RealityCapture software environment. The interconnection created a sufficiently detailed model represented by the mean projection deviation of 0.35 pixels for mesh formation. It should be noted that in the case of 3D model creation, the point cloud from laser scanning is used without RGB and from photogrammetry with RGB for texturing the model (function provided by the software). The point cloud forms the mesh that needs to be optimized. Performing this step is necessary since the mesh itself also contains invalid points resulting from too thick a point cloud. In this process, the predefined distance between two points was 2 mm, which, despite sufficient detail, affected the size of the object itself. With the integrated simplification function, the degree of optimization of the mesh model is considered until there is a significant loss of details. The resulting number of triangles that contained the model was 29.7 mil. Finally, unwrap was performed and the texture was applied in 4 K resolution and texture quality at the level of 80%, which gives the object a photorealistic look.