# RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction
## 4 Experiments
### Quantitative and Qualitative Results  
This section provides a quantitative and qualitative comparison with other methods on the PU-GAN [25] dataset and PU1K [17] dataset. The compared methods include PU-Net [14], MPU [16], PU-GAN [25], PU-GCN [17], Dis-PU [18], SSAS [31], and Grad-PU [38], where SSAS [31] is an unsupervised method, and the others are supervised methods. **PU-GAN**: We retrained the PU-Net [14], MPU [16], and SSAS [31] models on the PU-GAN [25] dataset. The remaining models, PU-GAN [25], PU-GCN [17], Dis-PU [18], and Grad-PU [38], utilized the pre-trained models released by the authors along with the data mentioned in the paper. Table 1 illustrates the quantitative comparison between RE-PU and other methods, where bold denotes the best performance, and underline denotes the second-best. Our input point cloud size is fixed at 2048 points. We achieved optimal results in both CD and EMD metrics and the second-best results in HD and P2F (std) metrics. Figure 7 illustrates the qualitative comparison between RE-PU and other methods. As seen in the first row showcasing the bird's claws, RE-PU exhibits finer details, with sharp features closely resembling the ground truth. In the second and third rows showcasing enlarged portions of fingers and models, RE-PU generates fewer noises and outliers, therebypreserving the original structure of the point cloud and closely approaching the ground truth in more intricate regions. **PU1K**: We retrained the models of PU-GAN [25], Dis-PU [18], and SSAS [31] on the PU1K [17] dataset, while the rest utilized the pre-trained models and data provided by the authors for PU-GCN [17] and Grad-PU [38]. Table 2 presents the quantitative comparison between RE-PU and other methods. Similar to the previous table, bold denotes the best performance, and italic indicates the second-best. The size of the input point cloud is also 2048. It can be observed that, although Grad-PU [38] performs well in CD and P2F (avg) metrics, RE-PU achieves the best results in other metrics, attaining the second-best results in the remaining two, with the output results closest to the target point cloud. Qualitative comparisons are depicted in Figure 8, where in the first and second rows, RE-PU exhibits fewer artifacts and better preserves the original structure of the point cloud. In the third row featuring the hat model, it is evident that RE-PU effectively preserves the original structure represented by the point cloud, while other methods generate more outliers and even attempt to merge different parts of the original point cloud. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **CD \(\downarrow\)** & **HD \(\downarrow\)** & **EMD \(\downarrow\)** & **P2F (avg) \(\downarrow\)** & **P2F (std) \(\downarrow\)** \\ \hline PU-Net [14] & 0.556 & 4.750 & 40.146 & 4.678 & 5.946 \\ MPU [16] & 0.298 & 4.700 & 30.534 & 2.855 & 5.180 \\ PU-GAN [25] & 0.280 & 4.640 & 26.243 & 2.330 & 4.431 \\ PU-GCN [17] & 0.258 & **1.885** & 24.460 & 2.721 & 3.542 \\ Dis-PU [18] & 0.260 & 2.104 & 25.312 & 2.480 & 3.521 \\ SSAS [31] & 0.264 & 2.320 & 25.027 & 2.625 & 3.462 \\ Grad-PU [38] & 0.245 & 2.369 & 23.348 & **1.893** & **2.875** \\ Ours & **0.238** & 2.012 & **22.353** & 2.463 & 2.965 \\ \hline \hline \end{tabular} Note: The downward arrows (\(\downarrow\)) indicate that lower values are better for the corresponding metrics.  
\end{table} 
Table 1: Quantitative results on PU-GAN [25] dataset.