# Point Cloud Upsampling Algorithm: A Systematic Review
## 6 Conclusions and Future Work  
In this paper, we conduct an extensive survey of point cloud upsampling algorithms. We mainly introduce the algorithms based on optimization and those based on deep learning. Although some achievements have been made in point cloud upsampling, there are still many unsolved problems. The point cloud upsampling algorithm needs further research and improvement. Future research can focus on the following aspects:  
1. **Network structure. ** (a) Feature extraction: This is a meaningful research direction to provide different scales of information for the upsampling component by improving the feature extraction component. (b) Upsampling component: At present, there are different forms of upsampling methods, and determining how to perform effective and efficient upsampling remains to be studied. (c) Coordinate reconstruction: The existing coordinate reconstruction method is relatively simple, and a significant amount of research should focus on exploration to improve the coordinate reconstruction method. (d) Optimizing the model structure: People are currently pursuing the model's performance more than paying attention to the size and calculation time of the model. Reducing the model size and speeding up the prediction while maintaining performance remains a problem. 2. **Loss function. ** In addition to designing a good network structure, improving the loss function can also improve the performance of the algorithm. The loss function establishes constraints between the low-resolution point cloud and the high-resolution point cloud, and optimizes the upsampling process according to these constraints. Commonly used loss functions include CD, EMD, and Uniform, which are often weighted and combined into a joint loss function in practical applications. For point cloud upsampling, exploring the potential relationship between low-resolution and high-resolution point clouds and seeking a more accurate and effective loss function is a promising research direction. For example, current point cloud upsampling algorithms have difficulties in filling large holes. Exploring suitable inpainting loss functions to constrain the generated point clouds to fill holes is a promising research direction. * **Dataset. ** At present, there is no universally recognized benchmark dataset. The datasets used by researchers for training and testing are very different, which is not conducive to the comparison between various models and subsequent improvement. Although very difficult, it is important to propose a high-quality benchmark dataset. * **Evaluation metrics. ** Evaluation metrics are one of the most basic components of machine learning. If performance cannot be accurately measured, it will be difficult for researchers to verify improvements. Point cloud upsampling is currently facing such a problem and requires more accurate metrics. At present, there is no unified and applied evaluation metrics for point cloud upsampling. Thus, more accurate metrics for evaluating upsampling quality are urgently needed. * **Unsupervised upsampling. ** As mentioned in Section 4.2, it is difficult to collect point clouds of the same object at different resolutions, and the low-resolution point clouds in the training set are often obtained by downsampling the real point clouds. Supervised learning may learn the inverse process of downsampling. Therefore, unsupervised upsampling of point clouds is a promising research direction. * **Applications. ** Point cloud upsampling can assist other point cloud deep learning tasks. For example, SAUM [51] uses a point cloud upsampling module to achieve point cloud completion, and HPCR [52] uses point cloud upsampling to improve the point cloud reconstruction effect. GeoNet [53] learns geodesic-aware representations and achieves better results by integration with PU-Net.