# Point Cloud Upsampling Algorithm: A Systematic Review
## 1 Introduction  
The point cloud is the standard output of 3D scanning. As a compact 3D data representation and an effective means of processing 3D geometric figures, point clouds have become more and more popular [1]. However, due to hardware limitations, 3D sensors such as LiDAR usually produce sparse, noisy, and non-uniform point clouds, especially for small objects or objects far away from the camera. This has also been proven in various public benchmark datasets, such as KITTI [2], SUN RGB-D [3], and ScanNet [4]. Although 3D sensing technology has made significant progress in recent years, it is still an expensive and time-consuming task to obtain a point cloud with high density and complete details. The sparsity and noise of point clouds affects their application in various fields, such as 3D shape classification, 3D object detection, and 3D object segmentation. Clearly, it is necessary to amend raw point cloud data.  
Point cloud upsampling means that the given sparse, noisy, and non-uniform point cloud is upsampled to generate a dense, complete, and uniform point cloud [5]. Under current conditions, this problem is very challenging. Unlike the image data representation in a computer, which usually encodes the spatial relationship between pixels, point cloud data are represented by a set of disordered data points. Therefore, there are many difficulties in upsampling point clouds, and there are few related works. With the development of deep learning, models such as PointNet [6] have provided new solutions for processing point cloud data. As a result, point cloud upsampling tasks have gradually attracted the attention of researchers.  
In this paper, we provide a comprehensive review of point cloud upsampling. We introduce optimization-based point cloud upsampling and deep learning-based point cloud upsampling, and focus on deep learning-based point cloud upsampling. Figure 1 shows the taxonomy of point cloud upsampling covered in this review in a hierarchically structured way. The main contributions of this work are as follows:1. We provide a comprehensive review of point cloud upsampling, including benchmark datasets, evaluation metrics, optimization-based point cloud upsampling, and deep learning-based point cloud upsampling. To the best of our knowledge, this is the first survey paper that comprehensively introduces point cloud upsampling.
2. We provide a systematic overview of recent advances in deep learning-based point cloud upsampling in a component-wise manner, and analyze the strengths and limitations of each component.
3. We compare the representative point cloud upsampling methods on commonly used datasets.
4. We provide a brief summary, and discuss the challenges and future research directions.  
The structure of this paper is as follows. Section 2 introduces the datasets and evaluation metrics for point cloud upsampling. Section 3 reviews the methods for point cloud upsampling based on optimization. Section 4 reviews the point cloud upsampling based on deep learning from the perspective of components. Section 5 compares and analyzes representative point cloud upsampling methods. Section 6 discusses future directions and open issues.