# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 4 Experimental Environment and Results
### Experimental Results
#### 4.3.2 Simplification Results  
An experiment was conducted to compare the original with the modified point-cloud. The values used for the sampling parameters were 30, 50, and 70% of the numbers. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multicolumn{2}{c}{**Voxel Size (k = 2.0)**} & \multicolumn{2}{c}{**Voxel Size (k = 1.55)**} & \multicolumn{2}{c}{**Voxel Size (k = 1.26)**} \\ \cline{2-7}  & **RMSE** & **RC** & **RMSE** & **RC** & **RMSE** & **RC** \\ \hline MAX & 1506 & 1.468 & 908 & 0.707 & 861 & 0.565 \\ AVG & 1443 & 1.453 & 875 & 0.706 & 830 & 0.564 \\ MIN & 1411 & 1.443 & 855 & 0.704 & 811 & 0.563 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE results according to voxel size. number of vertices in each source point-cloud was used as information to determine the number of data extracted from the source. Table 4 compares the results of the two traditional methods. The value indicates the file size. The SIM and SAM methods use the number of vertices in the simplification parameter. In the experiment, 30, 50, 70% of the original number of vertices were used. This is the suggested method, where the voxel sizes are respectively 2.0, 1.55, and 1.26. Measure the Max, Avg, and Min values as described in the experimental method. In addition, the size variation of the simplified file was recorded in the \"Dev\" item. The results of the smallest size reduction among the SIM, SAM, and PROP methods are displayed in bold. When the simplification parameter used was 30% (i.e., voxel size = 2.0), the average value showed the best performance in the proposed method. Additionally, the deviation value was 452, which was the lowest result. When the simplification parameter used was 50% (i.e., voxel size = 1.55), the average value showed the best performance with the MeshLab method. This is about 8% better than the proposed method. The experiment using the MeshLab method showed the highest performance on average, with a deviation of 17,464. When the simplification parameter used was 70% (i.e., voxel size = 1.26), the mean showed the best performance in the suggested method. In this case, the MeshLab minimum was confirmed, but this method results in higher deviations. Experiments have shown that the performance of the proposed method is on average high or slightly low. However, the proposed method is less variable than the other methods. This renders it advantageous in handling different shapes. The following figure provides a visual display of the comparison results. Figure 4 compares the results of experiments with the simplification parameter of a commercially available module at the 30% level. The first line shows the results of the MeshLab simplification. The result of the proposed method is shown on the second line. Figure 3a shows the overall rendering of a point-cloud representing a 2M volume. Figure 3b,c show the thin garment and its detailed results. Figure 3d,e show the body parts that should be represented in detail and their detailed results. Figure 3f,g focus on the areas where the clothing and body are identified together, and the body within which the details must be shown. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Categories**} & \multicolumn{3}{c}{**30\% (k = 2.0)**} & \multicolumn{3}{c}{**50\% (k = 1.55)**} & \multicolumn{3}{c}{**70\% (k = 1.26)**} \\ \cline{2-10}  & **SIM** & **SAM** & **PROP** & **SIM** & **SAM** & **PROP** & **SIM** & **SAM** & **PROP** \\ \hline Max & 42,311 & 36,575 & 22,263 & 88,216 & 60,957 & 54,061 & 90,648 & 85,341 & 69,241 \\ \hline Avg & 36,426 & 38,252 & 20,647 & 46,556 & 63,753 & 50,205 & 83,711 & 89,254 & 64,308 \\ \hline Min & 20,017 & 41,161 & 19,733 & 36,557 & 68,600 & 47,956 & 37,841 & 96,041 & 61,498 \\ \hline Dev & 7014 & 828 & 452 & 17,464 & 1381 & 1099 & 4955 & 1933 & 1413 \\ \hline \end{tabular}
\end{table} 
Table 4: Simplification result comparison table (unit: Kb).