# Advanced Methods for Point Cloud Processing and Simplification
## 8 Results
### Processing Results  
Table 1 summarizes the used detection parameters for all coordinate axes. The detection parameter selection depends on the quality of a used 3D scanner. Figure 18: Input point cloud of the flat with marked scan positions: (**a**) Individual 3D scans, green number in bracket is equal with the scanning positions in (**b**); (**b**) Flat top view scheme with marked measurement positions; (**c**) Composed final point cloud. In our case, due to the worse scanner calibration, the range _lvlS_ is higher. We can choose a bigger value of _lvlS_ and _lvlRS_, if we do not expect parallel surfaces close to each other in the selected range _lvlS_ -_lvlRS_. The level _dL_ detection by the histogram will always find the highest point concentration. In following Figure 19, the visualization of the flat by our proposed processing is shown with the utilization of the level image, allowing also the plane visualization (see reference [31]). The orientation of the planar surfaces is marked by three colors according to the surface orientation (X, Y, and Z). The detected planar surfaces with their physical parameters are described in the following tables (Tables 2-4). Presented planes in the X- and Y-axis have an area bigger than 5 m\({}^{2}\). The attributes denote from the left side: Idx--the detection level index; dL--the detected level in the scanned dimension; Pos--the coordinates of the image level origin in the space; A\({}_{\text{E}}\) the area of the planar surface plane; P\({}_{\text{E}}\) the perimeter of the plane; \(\sigma\)--the standard deviation of the points forming the planar surface; PC p.--the number of points representing the planar surface; I\({}_{\text{L}}\) px--the count of level image pixels; V--vertices of the plane; and P. decr.% expressing the decrease between the number of found vertices and the original points of the plane. \begin{table}
\begin{tabular}{c c c c} \hline
**Axis** & _lvlS_ & _lvlRS_ & _qD_ \\ \hline X & 0.1 m & 2.5 & 4 cm \\ Y & 0.1 m & 2.5 & 4 cm \\ Z & 0.2 m & 1.5 & 4 cm \\ \hline \end{tabular}
\end{table} 
Table 1: Detection parameter for all coordinate axes. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Idx** & **dL (m)** & **Pos (m)** & **A\({}_{\text{E}}\) (m\({}^{2}\))** & **P\({}_{\text{E}}\) (m)** & \(\sigma\) (m)** & **PC p.** & **I\({}_{\text{L}}\) px.** & **V** & **P. decr.\%** \\ \hline
2 & 0.313 & 0.313, 3.935, 3 & 7.174 & 19.33 & 0.0008 & 7306 & 4420 & 92 & 98.74 \\
4 & 3.521 & 3.52, 3.926, 3 & 9.862 & 18.177 & 0.0018 & 15,211 & 6148 & 71 & 99.53 \\
5 & 3.687 & 3.687, 2.581, 2.888 & 11.645 & 21.41 & 0.0006 & 14,460 & 7166 & 92 & 99.36 \\
14 & 7.162 & 7.162, 2.56, 2.96 & 13.278 & 20.833 & 0.0006 & 10,687 & 8112 & 87 & 99.19 \\
16 & 7.371 & 7.371, 4.454, 2.8 & 8.754 & 11.96 & 0.001 & 16,025 & 5450 & 43 & 99.73 \\
22 & 8.917 & 8.917, 0.291, 2.72 & 7.15 & 15.443 & 0.001 & 7424 & 4452 & 81 & 98.91 \\ \hline \end{tabular}
\end{table} 
Table 2: Detected planes with the physical description in the X-axis. Figure 19: Flat visualization with marked detected planes: red color marks plane detected in X-axis, green color is used for Y-axis, and blue color marks detected planes in the Z-axis. We can notice from the analysis of the tables, that indexes 4, 5 and 14, 16 in Table 2 show the ability of the scanning algorithm to detect parallel walls. The last row in all tables shows how many percentages of storage can be saved if we present a planar surface only by their vertices. The first planar plane surface in Table 3 is the floor of the flat. We can express the volume of the flat as the known area multiplied by the height of the flat. The planar surface 21, detected in the level 2.956 m, is the ceiling in Table 3. Then, the volume of the flat is approximately 165 m\({}^{3}\). The obtained results show the advantages of the proposed processing.