# Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases
## 4 Method for 2D Shape Analysis Based on Photogrammetric Models
### Method for Detecting Contours and Extracting Plane Sections Based on Point Segmentation
#### 4.2.4 Working with Point Cloud in the CAD Environment  
The previous work aimed to produce structured and comprehensible CAD-like layers of cross-sections for the visualization of the complex geometric structure. Data obtained by remote sensing techniques can be projected into raster images using either CloudCompare or the photogrammetric software Metashape which further allows for automatic contour generation. In addition, a created raster image can be converted into a vector using an external program such as AutoCAD Raster Design. [PERSON] et al. [49] explained in detail the process of creating a raster image and converting it into a vector plane. Figure 15 shows the automatically produced contours from the DEM generated for the 3D model of iconostases. It should be noted that in the case of the high-detail geometry of the iconostases, such a process would require an extensive amount of manual work for editing areas of difficult interpretation. The detected contours of the particular elements obtained by the previously proposed workflow presented sets of point clouds of a certain density. Considering CloudCompare allows for exporting a file in a DXF format, such point clouds can be easily visualized inside the Autodesk AutoCAD program [66]. The DXF data file format is a representation of drawing files that enables data interoperability between AutoCAD and other programs. As the DXF coordinates are without dimensions, when inserted into the AutoCAD, the point clouds need to be scaled properly to work with the appropriate drawing units. Figures 16 and 17 illustrate the AutoCAD DXF drawings of different parts of the iconostasis, sorted in  
Figure 14: The slices of points defining contours of different object elements from different object depth ranges: (**a**) ICONB and (**b**) ICONC. Figure 15: ICONB: example of the generated contours from the raster projection. separated layers according to their depth dimension. Inside a CAD environment, such a file format can be analyzed, measured, sectioned, annotated, and plotted as a technical drawing of the orthogonal section of the complex geometry. In addition, from such a graphic file format, the precise shapes of the particular elements can be redrawn by using an option to snap the cursor to 2D reference points in AutoCAD. Working with point cloud file formats inside the AutoCAD environment is also supported if they have been created in Autodesk ReCap [67] before being imported into AutoCAD. Thus, to open a point cloud in AutoCAD, the file format, such as PLY or LAS/LAZ, first must be imported into ReCap software and exported as the RCP file in order to be attached in AutoCAD. Figures 18 and 19 show the details that present ornaments of both iconostases, segmented from point clouds by using verticality geometric features. The details are given in a 1:50 scale ratio. It is noted that this kind of graphic visualizations allows for the identification of the shapes of the small-scale ornaments that are not easily recognizable from the colored point cloud. The level of detail and precision of the contour detection depends on the point cloud density. It is also noted that the resultant point densities of the  
Figure 16: ICONB. CAD layers of point clouds from different object depths, representing different parts of object geometry: (**a**) points extracted from the whole point cloud; (**b**) extracted points for main constructive elements; and (**c**) extracted points for ornaments. Figure 17: ICONC. CAD layers of point clouds from different object depths, representing different parts of object geometry: (**a**) points extracted from the whole point cloud; (**b**) extracted points for main constructive elements; and (**c**) extracted points for ornaments. segmented point clouds were slightly lower than the point density of the original cloud. The calculated point density for the first case study was 7.9 mm (see verticality label on Figure 18) and 3.9 mm for the second case study (see verticality label on Figure 19).