# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 4 Experiments
### Experimental
#### 4.3.1 PCN Dataset
The experimental results indicate that the ingenious integration of the LNDFL and the Transformer attention architecture in LNDM-Net enhances the model's completion ability for different samples. This integrated architecture devises a unique feature-extraction approach for point clouds of different scales. Compared to a single MLP architecture, it can more effectively extract local feature information and point-to-point correlation information from the input point clouds. Figure 6 presents a visual comparison of the completion effects of LNDM-Net and other algorithms for some categories in the PCN dataset. (1) When completing the local details of incomplete point clouds, LNDM-Net demonstrates a more precise ability to complete special structures. From the completion effect diagram of the Chair category in Figure 6, this chair is different from common ones as its backrest has multiple independent vertical beams. Although the original point cloud does  
Figure 6: The visualization of point cloud completion results of LNDM-Net and other algorithms for some categories in the PCN dataset. not have a missing structure for this part, only the seat surface of the chair is incomplete. After completion, the PF-Net algorithm can effectively fill in the missing part of the seat surface. However, it mistakenly treats the vertical beams on the backrest as incomplete parts, fills the gaps between the beams with point clouds of relatively low resolution, and, at the same time, retains the special texture structure of each beam. In contrast, based on completing the seat surface of the chair, LNDM-Net treats the texture information of the chair backrest as an intact part, effectively preserves the locally fine-grained structural information that is specially retained in the incomplete point cloud, and can also identify and effectively complete the missing parts. Similarly, this situation occurs in some samples from the Lamp category. From the completion effect diagram, the input incomplete point cloud has missing structures in the lamp base and lampshade parts. The PF-Net algorithm can effectively identify the incompleteness of the base and complete it. However, when completing, it fills the curved wire between the lamp base and the support rod and the hollow structure in the middle of the support rod with low-resolution point clouds while retaining the texture information. As a result, the completed sample seems to have a complete overall structure, but it cannot effectively filter out the special texture structures in the incomplete point cloud, affecting the special structural semantic information of the local structure. In contrast, after identifying the incompleteness of the base, LNDM-Net can effectively complete it. At the same time, when facing the hollow part in the support rod and the curved wire, it can effectively determine that these are intact parts, thus maximizing the retention of local feature structures during overall sample completion. (2) From the visualization results, it can be concluded that LNDM-Net shows good model generalization ability when facing special samples for completion. During the sample training phase, most samples within the same category have similar structures, but there are also samples with special structures. Since the number of training samples with special structures is relatively small compared to the majority of samples with similar structures, deficiencies will eventually be exposed during completion, affecting the completion effect. For example, in the Boat category samples in Figure 6, most boats have a long raised structure in the middle, especially when the sample structure is relatively long. However, when facing an incomplete point cloud of a boat with only a small raised part on the deck, as shown in the figure, the PF-Net algorithm can effectively complete the overall structure of the boat when it recognizes that only half of the boat's point cloud is incomplete.