## 3 Experiments and Results
### Experimental Results on MU-GAN
The effect of the traditional Poisson-based reconstruction is very close to the proposed method, whose generated point clouds are too uniform in every detail, resulting in a higher probability of alignment with the real data. However, the patches of the hole region generated by the traditional method do not conform to the feature distribution around the hole. The CD and EMD distance of the SU-GAN model in Table 1 are slightly higher than the MU-GAN model. It indicates that the generated results of the proposed method are more similar to the real point cloud. Multi-scale can obtain more dense and uniform repair results because the generator can obtain different levels of the geometric and semantic features. Figure 10 shows the qualitative results. We visualize the SU-GAN (e) and MU-GAN (f), where the SU-GAN has the same generator and discriminator structure as the MU-GAN. It demonstrates that simply applying a SU-GAN framework is insufficient for the CHarts completion problem when the hole is large (e.g., Line 1 and Line 2). As shown in Figure 10, our completion results are more consistent with the distribution of the incompleted point cloud and more realistic and denser. All the methods can achieve good repaired results for the small hole (e.g., Line 3). More importantly, we can repair the CHarts with complex structures. Compared with other methods, our method can achieve more completed results for large holes. The RL-GAN method can effectively complete 3D models with simple \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RL-GAN**} & \multicolumn{2}{c}{**PU-Net**} & \multicolumn{2}{c}{**Traditional**} & \multicolumn{2}{c}{**Our-SU**} & \multicolumn{2}{c}{**Our-Mu**} \\ \cline{2-11}  & **CD** & **EMD** & **CD** & **EMD** & **CD** & **EMD** & **CD** & **EMD** & **CD** & **EMD** \\ \hline head & 1.720 & 0.0915 & 1.710 & 0.0851 & 0.453 & 0.0883 & 0.788 & 0.0774 & **0.409** & **0.0728** \\ \hline body & 1.160 & 0.0753 & 0.665 & 0.0734 & 0.385 & 0.0685 & 0.528 & 0.0707 & **0.294** & **0.0676** \\ \hline arm & 1.940 & 0.0714 & 0.922 & 0.0742 & 0.218 & 0.0693 & 0.237 & 0.0688 & **0.208** & **0.0674** \\ \hline leg & 1.050 & 0.0692 & 1.024 & 0.0628 & 0.918 & 0.0696 & 0.563 & 0.0603 & **0.458** & **0.0599** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of CD (\(10^{-6}\) ) and EMD (\(10^{-1}\)) among our methods and several existing methods. Figure 9: (**a**) the CD value with different hole sizes; (**b**) the EMD value with different hole sizes. features and fewer points (e.g., airplanes), but due to the limitation of the network input, the downsampling of the original model causes the lack of features, resulting in a mismatch between the generated model and the original model, and the complete hole part is sparse. PU-Net is also an upsampling method like our method. It can increase the number of points in the original point cloud and can fill in smaller holes effectively. However, for larger holes, the increased number of points cannot be extended to the hole area. The method based on the Poisson equation is mainly for the reconstruction of the 3D mesh. The complete result of the model with more complex surface features is too smooth, and the generated points are too uniform to be combined with the original model features. In summary, for the CHarts model with a complex surface structure and more points, our method performs better-completed results, higher spatial integration, and more grained detail.