## 3 Experiment and Analysis
### Point Cloud Registration Experiment for Complex Target Object  
In this experiment, the point cloud models of the same person in different orientations are collected and then registered using different algorithms. Figure 4a shows the results of 3-D reconstruction. Figure 4b,c show the registration results of the traditional algorithm and the proposed algorithm, respectively. As can be seen from the figure, the blue point cloud and the red point cloud are more highly integrated in Figure 4c than that in Figure 4b, which can be known the proposed algorithm is more accurate than the traditional algorithm. Table 5 shows a comparison of the registration time of different algorithms. Table 6 shows the obtained rotation angle and the registration error distance in this experiment. The eight groups affine transformations are performed on the input point cloud data, which verify the reliability of the algorithm. Table 7 shows a comparison of the average registration error distance and the total registration time of the eight groups of experiments. The ratio of average registration error reduction is between 27.3% and 50%, and the ratio of total registration time reduction is about 1.1%. It can be seen that the average registration error distance of the proposed algorithm is smaller and the total registration time is shorter than the traditional one, which verifies the reliability of the proposed algorithm. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithms** & \begin{tabular}{c} **X-Direction Rotation** \\ **Angle (rad)** \\ \end{tabular} & \begin{tabular}{c} **Y-Direction Rotation** \\ **Angle (rad)** \\ \end{tabular} & \
\begin{tabular}{c} **Z-Direction Rotation** \\ **Angle (rad)** \\ \end{tabular} &
\begin{tabular}{c} **Average Error** \\ **Distance (cm)** \\ \end{tabular} \\ \hline Traditional algorithm & 0.186 & 0.018 & \(-\)0.625 & 0.158 \\ Proposed algorithm & 0.202 & 0.007 & \(-\)0.617 & 0.149 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of different algorithms. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Orientation** & **Algorithms** & \begin{tabular}{c} **Total Registration** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Initial Registration** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Accurate Registration** \\ **Time (s)** \\ \end{tabular} \\ \hline
1 & \begin{tabular}{c} Traditional \\ algorithm \\ \end{tabular} & 11.680 & 11.428 & 0.252 \\  & \begin{tabular}{c} Proposed \\ algorithm \\ \end{tabular} & 11.553 & 11.336 & 0.217 \\  & \
\begin{tabular}{c} Traditional \\ algorithm \\ \end{tabular} & 8.287 & 8.196 & 0.091 \\  &
\begin{tabular}{c} Proposed \\ algorithm \\ \end{tabular} & 8.029 & 7.955 & 0.074 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Registration time of different algorithms. Compared with the results obtained from the traditional algorithm, it is concluded that the proposed algorithm has higher registration accuracy and faster registration speed. Its advantages can be attributed to the following factors:  
1. The FPFH feature descriptor describes feature information accurately and comprehensively and avoids the errors in matching feature point pairs.