# Point Cloud Upsampling Algorithm: A Systematic Review
## 5 Algorithm Comparison and Analysis  
Since there is currently no recognized benchmark dataset, researchers typically choose to collect 3D models from existing public datasets for algorithm training and testing. In addition, there is no consensus on which evaluation metrics to use. This makes it difficult to compare different models. We selected the dataset provided by PU-GAN [13], which is relatively widely used. The dataset contains 147 3D models, in which 120 models were randomly selected for training, and the rest were used for testing. For EAR, we employed the released demo code to generate the results. For other deep learning-based upsampling models, we used their public code and retrained their networks with the dataset provided by PU-GAN. We conducted experiments on a NVIDIA 2080 Ti GPU. We chose CD, HD, P2F and uniformity as evaluation metrics to perform a simple comparative analysis among different algorithms. The smaller the evaluation metrics, the better. The quantitative comparison results are shown in Table 3.  
It can be seen that, in addition to the first deep learning-based network model, PU-Net, at present all methods based on deep learning are better than the best EAR algorithm based on optimization. The method based on deep learning has become the mainstream research direction of point cloud upsampling. For uniformity, the results of PU-Net are worse than those of EAR, which is due to its simple structure. Although the PointNet-based feature extraction method can combine global and local features, it does not perform well, and a simple multi-branch feature extension component also causes the generated points to be too similar to the input. At the same time, the point set generation component does not do much more than a set of MLPs. These factors lead to the poor uniformity of point clouds generated by PU-Net. The MPU introduces a multi-step progressive upsampling and uses dynamic graph convolution to  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Uniformity for Different \(\boldsymbol{p}(10^{-3})\)**} & \multirow{2}{*}{\(\boldsymbol{\boldsymbol{P2F}}\) (\(10^{-3}\))} & \multirow{2}{*}{\(\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{ \boldsymbol{ \boldsymbolboldsymbol{ }}}}}}}}}}}}}}}} & & \mathbf{\mathbf{\mathbf{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{ \boldsymbolboldsymbol  
extract point cloud features, which greatly improves the performance of the algorithm. PU-GAN further improves the performance of the upsampling algorithm in generating uniform point clouds by introducing uniform loss and adversarial loss. However, the GAN network is difficult to train and the design of a suitable discriminator is challenging. Currently, the best performer is PU-EVA, which interpolates new points by endowing geometric information of the target objects, and obtains the best results on the uniformity evaluation metric. The three metrics P2F, CD, and HD were used to evaluate the difference between the generated point cloud and the original point cloud. PU-GAN achieves the best performance on P2F, whereas PU-GCN achieves the best performance on CD and HD. The excellent performance of PU-GAN on P2F benefits from the up-and-down sampling structure and the adversarial training strategy, which makes the generated points closer to the surface of the object. NodeShuffle adopted by PU-GCN makes the generated point cloud closer to the original point cloud. In particular, for Dis-PU, although the results are not outstanding, the proposed disentangled refinement framework has great potential, and further research on this basis may achieve better results.