# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 4 Experiments
### Experimental  
#### 4.3.1 PCN Dataset  
The PCN dataset, derived from ShapeNet, serves as a benchmark dataset for point cloud completion tasks. It generates partial point clouds through back-projection from eight distinct viewpoints, simulating real-world sensor data acquisition. The dataset employs sampling strategies with 16,384 points representing complete shapes and 2048 points as partial inputs and is widely adopted for evaluating algorithm performance, primarily using Chamfer Distance (CD) as the evaluation metric. To scientifically evaluate the point-cloud completion performance of the proposed algorithm, Table 1 presents a comprehensive qualitative and quantitative comparison of the experimental results between LNDM-Net and several existing point-cloud completion models. Two key evaluation metrics are utilized in this comparison: \(d_{CD}\)(\(S1\)\(\rightarrow\)\(S2\)) and \(d_{CD}\)(\(S2\)\(\rightarrow\)\(S1\)). Specifically, \(d_{CD}\)(\(S1\)\(\rightarrow\)\(S2\)) represents the average Euclidean distance from each point in the output point cloud to the nearest point in the real point cloud. This metric is primarily used to assess the positional and shape differences between the generated point cloud and the real point cloud, providing insights into the proximity of the output to the true 3D shape. On the other hand, \(d_{CD}\)(\(S2\)\(\rightarrow\)\(S1\)) describes the average Euclidean distance from each point in the real point cloud to the nearest point in the output. This metric evaluates how well the generated point cloud covers the real point cloud, effectively determining whether the model has captured the shape and features of the true point cloud. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{5}{c}{\(d_{CD}\)(\(S1\)\(\rightarrow\)\(S2\))/\(d_{CD}\)(\(S2\)\(\rightarrow\)\(S1\))} \\ \cline{2-6}  & **LGAN-AE [9]** & **PCN [12]** & **3D-Capsule [14]** & **PF-Net [17]** & **MSTCN [3]** & **LNDM-Net** \\ \hline Plane & 1.047/0.500 & 0.480/0.914 & 0.604/1.344 & 1.752/1.262 & 2.364/1.299 & **0.983/0.347** \\ Boat & 2.401/1.482 & 1.165/3.092 & 4.178/3.870 & **1.103**/2.336 & 3.150/3.949 & 1.781/**2.886** \\ Car & 3.149/4.189 & 3.485/4.528 & 2.141/4.691 & 2.293/1.818 & 4.705/2.914 & **1.541/0.755** \\ Chair & **2.012**/3.661 & 2.742/2.001 & 2.108/2.131 & 2.473/2.231 & 3.796/2.226 & 2.082/**1.498** \\ Lamp & 3.020/3.463 & 1.805/2.119 & 2.342/2.416 & **1.705**/1.941 & 4.161/5.244 & 1.431/**1.328** \\ Cabinet & 0.819/1.271 & 2.760/2.462 & 2.294/3.009 & 0.583/**0.651** & 2.517/1.509 & **0.478**/0.752 \\ Couch & 1.839/2.216 & 2.136/2.163 & 1.723/1.927 & 1.722/1.857 & 2.032/1.532 & **1.577/1.202** \\ Table & 2.236/2.543 & 2.848/4.125 & 3.994/4.868 & 2.662/2.525 & 4.249/2.362 & **2.010/2.341** \\ \hline average & 2.124/2.416 & 2.178/2.676 & 2.423/3.032 & 1.749/1.828 & 3.372/2.629 & **1.610/1.389** \\ \hline \hline \end{tabular}  
* **Note:** The bolded font represents the optimal value for each row.  
\end{table} 
Table 1: Experimental data of different completion algorithms on the PCN dataset. By employing both of these metrics, a more comprehensive and objective evaluation of LNDM-Net's point-cloud completion performance can be achieved, enabling a scientific and rational comparison with existing models. Through an in-depth analysis of the experiments on the PCN dataset in Table 1, it can be clearly observed that LNDM-Net exhibited excellent performance in terms of the average CD value of the eight-class samples on the PCN dataset, outperforming other algorithms. Specifically, compared with the PF-Net algorithm, the performance improvement of LNDM-Net reaches approximately 5.78%. A further breakdown of each category reveals that LNDM-Net demonstrates better \(d_{CD}\) values for four types of objects such as tables and planes. Moreover, for the other 10 types of objects like guitars and hats, the \(d_{CD}(S1{\rightarrow}S2)/d_{CD}(S1{\rightarrow}S2)\) values of LNDM-Net are also superior to those of other networks.