# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 4 Experiments
### Performance Analysis of Point Cloud Part Segmentation

Table 3: Segmentation results of different methods on ShapeNet dataset. Figure 7: The part of the point cloud dataset and its corresponding training samples, the different colors represent different part of objects. the network in this paper proposes a local domain relational convolution like that in the RS-CNN. The higher values of IoU are cap, chair, guitar, lamp, knife, laptop and mug, and the overall effect of part segmentation of these categories is better. The proposed method obtains the highest value of IoU in the categories of aero, chair, lamp, pistol, table and laptop, as shown in Figure 8.  
From Figure 8, it is clearly visible that different parts of the six categories are accurately segmented by the proposed method. For aero, the airfoils are segmented exactly, and they display different colors on fuselage. Especially for the second aero, the engines, fuselage, menopause and airfoils display different colors. For chair, although there are very many styles of chairs, these chairs are accurately segmented. The first chair displays different colors for the back, surface and legs. For lamp, the bulb, holder, base and shade are accurately segmented by the proposed method. For pistol, the barrel, grip and trigger display different colors. Some points in trigger are misidentified as the barrel or grip. The possible reason for this is that the trigger part has fewer points, and it is a difficult-to-extract feature. The table top and legs are segmented exactly by the proposed method. Since the structure of laptops is very simple, it is easy to segment laptops, and this category has the highest segmentation accuracy among the 16 categories. From Figure 9, it is clearly visible that the features learned by the edge convolution layer are mainly about simple features such as edges, corners and radians, while the relation convolution layer captures more complex semantic shape features such as chair backtests, table legs, knife handles and hat prims. The proposed method combines these two convolutional layers and has better performance in point cloud part segmentation by feature extraction. Figure 8: Part segmentation of six categories by the proposed method, the different colors represent different part of objects. Figure 9: Point cloud feature learning at edge and relational convolution layers, the different colors represent different part of objects. We used the proposed method to conduct the part segmentation of airplane, chair, guitar and pistol, and compared the segmentation results with the PointNet, DGCNN and RS-CNN methods, as shown in Figure 10. Each category is labeled with several different colors, and they represent the different components in each category. For the airplane, the four methods can accurately segment the engines, fuselage, empennage and airfoils of airplane. However, the PointNet, DGCNN and RS-CNN methods misidentify a small number of partial points on the airfoil as engine points, as shown in the ellipse of Figure 10a. The chair legs are segmented by the four methods. However, the PointNet method misidentifies points of the back of a chair as points of the seat. For the RS-CNN and DGCNN methods, a few points on the back of chair are mistakenly recognized as the seat points, as shown in the rectangle of Figure 10. The guitar mainly includes the head, neck and case, and they are successfully segmented by the four methods. However, the segmentation effect of these four methods is different. In particular, a few of points on the neck are taken for the head of the guitar by the PointNet and DGCNN methods. Although the RS-CNN and proposed method keep a few points on the head that do not belong to this part, the head of the guitar segmentation by these two methods is superior to the PointNet and DGCNN methods, as shown in the circle of Figure 10. For the pistol, it is clearly visible that it is segmented into three parts. The square of Figure 10 shows that the trigger points segmented by PointNet, DGCNN and RS-CNN belong to other parts.