# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 4 Experiments
### Performance Analysis of Point Cloud Classification  
In order to verify the classification performance of the proposed network model, we used the proposed method to conduct the point cloud classification and compare it with other network models, as shown in Table 1. From Table 1, it can be seen that the mAcc and OA of the proposed method are 91.4% and 93.8%, respectively. By comparing with other networks, it is clearly visible that the mAcc and OA of the proposed method are higher than other networks. Among them, the mAcc of the proposed method is slightly higher than DGCNN, GAPNet and PointNet++, and obviously higher than PointNet and SO-Net. The OA of the proposed method is similar to DGCNN, RS-CNN, PointConv and GAPNet, and they are all greater than 92%. However, the OA of the proposed method is still slightly higher than these four methods. Specifically, compared with PointNet, PointNet++, SO-Net and MSHANet, the OA of the proposed method is improved by 5%, 2%, 3% and 2.6%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Points** & **mAcc (\%)** & **OA (\%)** \\ \hline PointNet & 1024 & 86.2 & 89.2 \\ PointNet++ & 1024 & 89.4 & 91.9 \\ DGCNN & 1024 & 90.2 & 92.9 \\ RS-CNN & 1024 & — & 92.9 \\ SO-Net [34] & 5000 & 87.3 & 90.9 \\ PointConv [35] & 1024 & — & 92.5 \\ GAPNet [36] & 1024 & 89.7 & 92.4 \\ MSHANet [37] & 1024 & — & 91.3 \\ Proposed method & 1024 & 91.4 & 93.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Point cloud classification of different methods on the ModelNet40 dataset. According to Table 1, the classification accuracies of different networks are obtained, as shown in Figure 5. From Figure 5, it is clearly visible that the classification accuracies of most objects in this paper are higher than when using other methods. For a few different objects, such as bathtub, guitar, tent and Xbox, the classification accuracy of the proposed method is slightly lower than that of the RS-CNN method. The possible reason is that the RS-CNN proposes a convolutional operator RS-Conv that can learn the geometric topological relations between points, allowing the RS-CNN to reason about the spatial layout of points and achieve very powerful shape context awareness. And the network in this paper is weaker than the RS-CNN for spatial layout inference of points. For the bed, bench, bowl, cup, door, glass box, tv stand and Xbox, the classification accuracy of the proposed method is slightly lower than that of the GAPNet method. The reason for this phenomenon is that GAPNet learns the local semantic information of the original input point cloud by embedding a graphical attention mechanism in the stacked MLP layers. Moreover, the network uses a parallel mechanism (multi-head attention) to generate attention features from different GAP-Layer layers in an aggregated manner, enabling the network to efficiently extract local geometric features of disordered point clouds. In contrast, the proposed method does not use the attention mechanism module and does not acquire the multi-attentive and multi-graph features of the point cloud, which leads to the poor classification accuracy of some objects. Although most of the objects are successfully classified by these methods, there are a few objects that cannot be accurately classified, such as the flower pot, radio and wardrobe. From Figure 5, it can be seen that all these methods have the lowest classification accuracies for the flower pot, which are less than 40%. The reason is that there are so many styles of flower pots and some of them have very complex structures, so that the network is not able to effectively identify the complex structures of flower pots. Also, the structure of the flower pot is similar to the bottle, vase and lamp, and sometimes they are classified into other similar structures. Although the classification accuracy of the radio in this paper is obviously higher than other methods, they are all lower than 70%. By the same token, the classification accuracies of the wardrobe by these methods are low.