# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 4 Experiments
### Performance Analysis of Point Cloud Semantic Segmentation  
In order to verify the effectiveness of the proposed method, the semantic segmentation results of the S3 DIS dataset were obtained by the proposed method and compared with other networks, as shown in Table 4.  
From Table 4, it is visible that the DGCNN is much less effective than the proposed method for segmenting objects such as wall, window, door and bookcase. A possible reason is that the DGCNN only constructs the local neighborhoods once by using graph convolution networks. The RS-CNN achieved an accuracy of 68.7 in segmenting windows, and it is the highest segmentation accuracy compared to the other networks. However, the segmentation accuracies of sofa, board and clutter are lower than the proposed method. The possible reason is that the RS-CNN method ignores the dependence of the shape feature between domain points when describing local shape features. The overall segmentation performance of the proposed method is superior to other networks, and it can achieve the best segmentation results on 8 of the 13 categories. In particular, the proposed method obtains better segmentation accuracy in categories such as ceiling, floor and wall. The reason is that the proposed method is able to fully extract point cloud features. From Table 4, it is clearly visible that the OA and mIoU of the proposed method are 5.4% and 8.2% higher than the DGCNN and 1.7% and 2.3% higher than the RS-CNN. The OA and mIoU of the proposed method are slightly lower than Point Transformer. The reason is that the Point Transformer network has a strong ability of semantic segmentation for the door and chair, and the semantic segmentation accuracy of the door and chair obtained by the Point Transformer network is much higher than other methods. Some of the segmentation results of the proposed method on the S3 DIS dataset were obtained, as shown in Figure 11. The proposed method can more accurately segment the objects such as chairs, tables, etc., as shown in the dashed rectangle of Figure 11. It illustrates that the proposed method can identify the location range of the objects and mitigate the interference of incorrect classification with the correct results. Simultaneously, the leg part of a chair that connects to the floor is also accurately segmented by the proposed method. The reason for this result is that the proposed method enhances the ability to identify the detailed feature information of the sampled points and is able to determine the boundary range of an object more accurately. However, the proposed method cannot accurately segment a small number of objects that are close to each other, as shown in the ellipse of Figure 11. A possible reason for this phenomenon is that the local features of these two types of objects are very similar. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **PointNet** & **PointCNN [42]** & **DGCNN** & **RS-CNN** & **Kpconv [43]** & \
\begin{tabular}{c} **Point Transformer** \\ **[**44**]** \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline ceiling & 88.0 & 94.8 & 92.9 & 93.4 & 93.6 & 94.3 & 94.9 \\ floor & 88.7 & 97.3 & 93.8 & 95.5 & 92.4 & 97.5 & 97.6 \\ wall & 69.3 & 75.8 & 73.1 & 84.0 & 83.1 & 84.7 & 84.9 \\ beam & 42.4 & 63.3 & 62.5 & 61.9 & 63.9 & 55.6 & 63.8 \\ column & 23.1 & 51.7 & 55.9 & 57.6 & 54.3 & 58.1 & 58.9 \\ window & 47.5 & 58.4 & 57.6 & 68.7 & 66.1 & 66.1 & 68.6 \\ door & 51.6 & 57.2 & 59.2 & 66.5 & 76.6 & 78.2 & 68.8 \\ chair & 42.0 & 69.1 & 66.7 & 65.2 & 57.8 & 74.1 & 67.7 \\ table & 54.1 & 71.6 & 60.4 & 75.9 & 64.0 & 77.6 & 77.9 \\ bookcase & 38.2 & 61.2 & 57.0 & 68.6 & 69.3 & 71.2 & 71.5 \\ sofa & 9.6 & 39.1 & 54.8 & 62.9 & 74.9 & 67.3 & 64.5 \\ board & 29.4 & 52.2 & 56.7 & 60.1 & 61.3 & 65.7 & 65.8 \\ clutter & 35.2 & 58.6 & 51.6 & 59.1 & 60.3 & 64.8 & 64.9 \\ OA & 78.5 & 88.1 & 84.1 & 87.8 & – & 90.2 & 89.5 \\ mIoU & 47.6 & 65.4 & 64.8 & 70.7 & 70.6 & 73.5 & 73.0 \\ \hline \hline \end{tabular}
\end{table}