# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 4 Experiments
### Experimental
#### 4.3.4 Ablation Experiment  
The innovation of the network model proposed in this paper lies in the targeted improvement of three key modules: the feature extraction module, the attention layer architecture, and the attention discriminator. To thoroughly assess the effectiveness of these modules in practical applications, an ablation experiment was conducted using the PCN dataset. In this experiment, four network models were compared: the complete LNDM-Net model (which includes all the enhanced modules) and three control models (each removing one of the key modules mentioned earlier). By comparing the performances of these models under identical conditions, the contribution of each module to the overall performance can be intuitively evaluated. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **PCN [12]** & **PF-Net [17]** & **LGAN-AE [9]** & **GR-Net [27]** & **LNDM-Net** \\ \hline FD (\(\times 10^{-2}\)) [27] & 23.52 & 31.81 & 4.75 & 8.16 & **2.91** \\ MMD (\(\times 10^{-2}\)) [27] & 31.73 & 7.89 & 25.2 & 6.78 & **6.57** \\ \hline \hline \end{tabular}  
**Note**: The bolded font is the optimal value for each line.  
\end{table} 
Table 3: Completion results on the KITTI dataset. Figure 7: RGB image and incomplete point cloud completion results within the same scene of the KITTI dataset. Table 4 provides detailed results from the ablation experiment, and Figure 8 visually illustrates the point cloud completion results for the Plane category. From the results, it is evident that Experiment [D] (the full LNDM-Net model) achieved the lowest CD value. In this experiment, the model not only effectively completed the missing right-wing engine but also processed the point-cloud distribution at the junction between the wing and the fuselage, making it highly consistent with the corresponding part of the real point cloud. This result not only demonstrates the exceptional performance of Experiment [D] in the point cloud completion task but also emphasizes the importance and necessity of maintaining the integrity of the LNDFL, the attention layer, and the attention discriminator. Through comparative analysis of the ablation experiment, it can be concluded that the proposed network model significantly enhances point-cloud completion performance, with each key module playing an indispensable role. This conclusion provides strong support for future research and offers valuable insights for researchers in related fields. Based on the comprehensive analysis of the visualization results and experimental data, the following specific conclusions can be drawn:  
In Experiment [A], when the other parts of LNDM-Net were retained and only the feature-extraction module of this model was disabled, the average CD value increased significantly to 1.742/1.854. This change was particularly evident in the completion effect of the contour of the right-wing engine of the plane and the connection between the right wing and the fuselage. Specifically, the completion results lacked clarity, and the contour was blurred. This result further validates the notion that the introduction of the feature-extraction module, especially for the improvement of the local-detail completion ability of incomplete point-cloud samples, has a significant effect. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Number** & **Network Model** & \(d_{\text{CD}}\)**(S1\(\rightarrow\)S2)/\(d_{\text{CD}}\)**(S2\(\rightarrow\)S1)** \\ \hline A & Overall/LNDFL & 1.742/1.854 \\ B & Overall/Attention & 2.392/2.541 \\ C & Overall/Attention discriminator & 2.197/2.293 \\ D & Overall & **1.6951.206** \\ \hline \hline \end{tabular}  
* **Note**: /X means missing part X.  
\end{table} 
Table 4: Comparison results of the ablation experiment. Figure 8: Visualization of the ablation experiment results for the Plane category in PCN. In Experiment [B], when all components of LNDM-Net were retained except for the bias-attention layer in the feature-extraction process, the average CD value increased significantly to 2.392/2.541.