# Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases
## 4 Method for 2D Shape Analysis Based on Photogrammetric Models
### Iconostases' Photogrammetric Digitization
#### 4.1.2 Photogrammetric 3D Reconstruction  
The photographs acquired by the surveying were automatically processed using the photogrammetric software Agisoft Metashape [63]. To produce a highly detailed and accurate 3D reconstruction while maintaining an optimal software performance speed, the image alignment and dense cloud generation were processed with high parameters. The main result of the data processing was a dense point cloud. The precision of the overall reconstruction was estimated after scaling the 3D model properly. The 3D model was scaled within the Agisoft Metashape software by introducing control points (CPs) into the generated 3D reconstruction. The object dimensions were collected through on-site measurements. The calculated round square meter (RMS) errors showed the difference between the real distances and the distances obtained in the 3D models (Table 2). Based on the dense point cloud, the point density of the 3D model can be determined. To calculate the point density, the DEM was also generated. The DEM resolution presents the point density of the produced point cloud. The achieved accuracies of the reconstructions that resulted from using high-quality processing settings are shown in Table 2. The accuracy of the overall reconstructions fell within the limits of the initially determined GSD value (Table 2). The resolution of the DEM depends on the quality parameter set during the construction of the dense cloud. Therefore, considering the dense clouds were generated with a high parameter (instead of ultra-high), the achieved DEM resolution was half the value of the GSD (Table 2). The point density was important for further analysis of the precision of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Surveying Parameters** & **ICONB** & **ICONC** & **ICONT** \\ \hline Object dimensions (m) & 9.75 (w) \(\times\) 12.00 (h) & 7.65 (w) \(\times\) 7.40 (h) & 6.65 (w) \(\times\) 7.00 (h) \\ Object area (m\({}^{2}\)) & 117 & 56.61 & 42.56 \\ \(h\) = shooting distance (m) & 12.5 & 7.5 & 7.5 \\ \(f\) = focal length (mm) & 24 & 24 & 24 \\ \(m\) = scale (m) & 0.523 \(\times\) 10\({}^{-3}\) & 0.314 \(\times\) 10\({}^{-3}\) & 0.314 \(\times\) 10\({}^{-3}\) \\ GSD \({}^{1}\) (mm) & 2.5 & 1.5 & 1.5 \\ \hline \(D\) = the object space covered & 12.3 (portrait mode) & 7.4 (portrait mode) & 7.4 (portrait mode) \\ by a photograph (m) & 8.2 (landscape mode) & 4.9 (landscape mode) & 4.9 (landscape mode) \\ \hline Camera mode & Portrait & Portrait & Portrait \\ \(b\) = baseline (m) & 0.70 & 0.50 & 0.50 \\ ISO & 100 & 100 & 100 \\ Aperture (\(f\)-stop) & f/5.6 & f/5.6 & f/8 \\ Shutter speed & 1.6 s & 1 s & 1 s \\ Focus point (m) & 12.5 & 7.5 & 7.5 \\ \hline DoF \({}^{2}\) (m) & 3.70 (near distance) & 3.09 (near distance) & 2.50 (near distance) \\ infinite (far distance) & infinite (far distance) & infinite (far distance) \\ \hline \hline \end{tabular}  
\({}^{1}\) ground sample distance. \({}^{2}\) depth of field.  
\end{table}
Table 1: Comparison of surveying data between the three case studies.  
contour detection during the process of point cloud segmentation. The textured meshes were also generated (Figure 4) and used in the further application for sampling points and creating a new point cloud.