# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 4 Experiments
### Performance Analysis of Point Cloud Part Segmentation  
In order to evaluate the effectiveness of the proposed method in the field of part segmentation, we used the ShapeNet dataset to conduct the part segmentation, and the part of the point cloud dataset and its corresponding training samples are shown in Figure 7. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **mAP** \\ \hline PointNet & 70.5 \\ PointNet++ & 81.3 \\ DGCNN & 83.2 \\ RS-CNN & 85.3 \\ Proposed method & 88.7 \\ \hline \end{tabular}
\end{table} 
Table 2: Shape retrieval results of different methods on the ModelNet40 dataset. Figure 6: Shape retrieval of the proposed method and PointNet on ModelNet40 dataset, and the red squares represent wrong classification results. The segmentation results of the proposed method on the ShapeNet dataset were compared with other networks. Table 3 gives the cross-merge ratio and average cross-merge ratio of each network for 16 categories. From Table 3, it is visible that the proposed method has the highest cross-merge ratios for six categories. Among them, the cross-merge ratios of the chair and pistol obtained by the proposed method are obviously higher than the other methods. The possible reason is that the structure of the chair and pistol is relatively simple. The segmentation accuracy of the chair and pistol is higher than other methods. The reason is that the proposed method enhances the contextual information of the local features of the chair and pistol by further constructing the local domain and captures higher-level semantic shape information. Although the cross-merge ratios of other categories obtained by the proposed method are not the highest, they show only small differences from the highest IoU. For example, the cross-merge ratios of the knife and rocket obtained by the proposed method are almost the same as the highest cross-merge ratio. According to the cross-merge ratios of different categories, we obtained the mIoU of different methods. The comparison results show that the mIoU of part segmentation by the proposed method is 86.3%, which is 2.9%, 1.4%, 1.7%, 1.7%, 1.2%, 0.1% and 1.0% higher than PointNet, RS-Net, SCN, SPLATNet, DGCNN, RS-CNN and LRC-NET, respectively. The mIoU of the part segmentation of the proposed method is almost the same as that of the RS-CNN. The reason for this phenomenon is that \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**PointNet**} & \multirow{2}{*}{**RS-Net [38]**} & \multirow{2}{*}{**SCN [39]**} & \multirow{2}{*}{**SPLATNet [40]**} & \multirow{2}{*}{**DGCNN**} & \multirow{2}{*}{**RSCNN**} & \multirow{2}{*}{**LRC-NET [41]**} & \multirow{2}{*}{
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} } \\ \hline aero & 2690 & 83.4 & 82.7 & 83.8 & 81.9 & 84.0 & 83.5 & 82.6 & 84.5 \\ bag & 76 & 78.7 & 86.4 & 80.8 & 83.9 & 83.7 & 84.8 & 85.2 & 86.1 \\ cap & 55 & 82.5 & 84.1 & 83.5 & 88.6 & 84.4 & 88.8 & 87.4 & 88.6 \\ car & 898 & 74.9 & 78.2 & 79.3 & 79.5 & 77.8 & 79.6 & 79.0 & 79.3 \\ chair & 3758 & 89.6 & 90.4 & 90.5 & 90.1 & 90.6 & 91.2 & 90.7 & 92.4 \\ ear phone & 69 & 73.0 & 69.3 & 69.8 & 73.5 & 74.4 & 81.1 & 80.2 & 80.9 \\ guitar & 787 & 91.5 & 91.4 & 91.7 & 91.3 & 91.0 & 91.6 & 91.3 & 91.5 \\ knife & 392 & 85.9 & 87.0 & 86.5 & 84.7 & 88.1 & 88.4 & 86.9 & 88.3 \\ lamp & 1547 & 80.8 & 83.5 & 82.9 & 84.5 & 83.4 & 86.0 & 84.5 & 86.6 \\ laptop & 451 & 95.3 & 95.4 & 96.0 & 96.3 & 95.8 & 96.0 & 95.5 & 96.9 \\ motor & 202 & 65.2 & 66.0 & 69.2 & 69.7 & 67.8 & 73.7 & 71.4 & 73.2 \\ mug & 184 & 93.0 & 92.6 & 93.8 & 95.0 & 93.3 & 94.1 & 93.8 & 94.7 \\ pistol & 283 & 81.2 & 81.8 & 82.5 & 81.7 & 82.3 & 83.4 & 79.4 & 84.9 \\ rocket & 66 & 57.9 & 56.1 & 62.9 & 59.2 & 59.2 & 60.5 & 51.7 & 62.8 \\ skate board & 152 & 72.8 & 75.8 & 74.4 & 70.4 & 76.0 & 77.7 & 75.5 & 77.5 \\ table & 5271 & 80.6 & 82.2 & 80.8 & 81.3 & 81.9 & 83.6 & 82.6 & 83.9 \\ mIoU & 83.4 & 84.9 & 84.6 & 84.6 & 85.1 & 86.2 & 85.3 & 86.3 \\ \hline \hline \end{tabular}
\end{table}