## 3 Experimental Result  
In order to verify the performance of the proposed algorithm, the point cloud data of the three-dimensional morphology of different types of warheads were obtained, respectively. The actual bullet point cloud collected on the automatic zoom three-dimensional surface measuring instrument and the bullet point cloud obtained from the CAD model were taken as the experimental data. The proposed algorithms, ICP, TRICP, and GOICP,were used for the registration, and the experimental results were analyzed. The experimental platform was the Intel(R) Core (TM) i5-8250U CPU @ 1.60 GHz 1.80 GHz processor (Intel, Santa Clara, CA, USA), with 8 GB of memory, using Windows 10, a 64-bit operating system, and a Matlab 2022b coding environment. The initial position of the bullet point cloud sampled by the CAD model is shown in Figure 5, where the 9 mm_source is the source point cloud, represented in green, and the 9 mm_target is the target point cloud, represented in blue. The algorithm proposed in this paper can simplify the dense point cloud to the specified resolution. Table 1 shows the point cloud filtering results when the target size is specified as 0.003 mm. After two voxel filtering operations, the point cloud can be automatically adjusted to the specified size, and the resolution of the source point cloud is basically the same as that of the target cloud, with an error of about 1.4%. The registration results of the bullet point clouds under different overlap rates are shown in Table 2. The CAD model of the bullet is similar to that of five bullet point clouds generated by the three-dimensional topography measurement from five angles, as shown in Figure 6, with different overlap rates among the point clouds. The definition is based on local density comparison of the points in the two aligned point clouds to assess their spatial overlap degree. By calculating the local density of each point within a given search  
Figure 5: Initial location map of bullet point cloud (blue point cloud is source point cloud, green electric cloud is target point cloud): (**a**) sampling point cloud map of the 9 mm bullet CAD model to be registered (overlap rate of 52.85%); (**b**) cloud image of the sampling point of the CAD model of the 9 mm bullet to be registered (overlap rate of 33.02%); (**c**) automatic zoom microscope acquisition of the 7 mm bullet point cloud initial location map to be registered; (**d**) the initial location map of the 9 mm bullet point cloud acquired by the autozoom microscope to be registered. radius and comparing the density ratio of the corresponding pair of points, the two points are considered to overlap when the local densities of the two points are similar. The ratio of the total number of overlapping points to the total number of points in the point cloud is defined as the overlap rate. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**9 mm\_Source**} & \multicolumn{2}{c}{**9 mm\_Target**} & \multicolumn{2}{c}{**5 mm\_Source**} & \multicolumn{2}{c}{**5 mm\_Target**} \\ \cline{2-9}
**Number of** & **Point** & & **Point** & & **Point** & & \\
**Iterations** & **Cloud** & **Resolution** & **Cloud** & **Resolution** & **Cloud** & **Resolution** & **Cloud** & **Resolution** \\  & **Number** & & **Number** & & **Number** & & **Number** & \\ \hline
0 & 1,000,541 & 0.005 & 100,325 & 0.014 & 3,102,272 & 0.004 & 96,511 & 0.011 \\
1 & 111,926 & 0.020 & 60,858 & 0.022 & 69,571 & 0.020 & 44,764 & 0.021 \\
2 & 84,850 & 0.025 & 49,403 & 0.026 & 54,297 & 0.024 & 34,804 & 0.025 \\ \hline time- & \multirow{2}{*}{12.3090} & \multirow{2}{*}{1.5058} & \multirow{2}{*}{8.5035} & \multirow{2}{*}{1.2243} \\ consuming (s) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Voxel downsampling filter number results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & Textual algorithm & ICP & TRICP & GOICP \\ \hline
52.85\% & & & \\
41.36\% & & & \\ \hline \hline \end{tabular}
\end{table}