# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Supervised Upsampling
#### 4.1.1 Feature Extraction Components  
The first step in point cloud upsampling using deep learning models is to extract point cloud features. Several different feature extraction components are introduced here. PointNet-Based Feature Extraction  
[PERSON] et al. [5] proposed the first deep learning model for point cloud upsampling, PU-Net, in which two feature learning strategies are used, hierarchical feature learning and multi-level feature aggregation. For hierarchical feature learning, PU-Net adopts the hierarchical feature learning mechanism proposed in PointNet++ [27] as the frontal part of the network. In order to obtain more of the local context, PU-Net specifically uses a relatively small grouping radius in each layer. For multi-level feature aggregation, PU-Net first uses the interpolation method in PointNet++ to upsample the downsampled point features in hierarchical feature learning and restore all original point features. Then it uses convolution to reduce the dimensionality of the interpolated features at different levels to the same dimension. Finally, the features of each level are concatenated as embedded point features. DensePCR [29] and EC-Net [12] also use similar feature extraction strategies. [PERSON] et al. [30] proposed spatial feature extractor block (SFE block) to replace PointNet++ to extract local features. Compared with PU-Net's point feature embedding, SFE block exploits local point relationships to extract rich local details. In particular, each point in the local region has different effects on the local spatial features, which represent different spatial distributions of the local geometry. After combining point-to-point features, the extracted features from the point cloud and local geometry can be captured more accurately. The feature extraction method based on PointNet can combine global and local features, but requires an additional point set downsampling and interpolation process, which consumes more computing resources. Graph Convolution-Based Feature Extraction  
[PERSON] et al. [16] proposed a multi-step point cloud upsampling network (MPU), which was inspired by dynamic graph convolution to define local neighborhoods in feature space. Point features are extracted from local neighborhoods through a k-nearest neighbors (kNN)  
Figure 2: Schematic diagram of a point cloud upsampling model based on deep learning. \(C\) and \(C^{\prime}\) are the features of the point, \(r\) is the upsampling rate. search based on feature similarity. This method does not require point set subsampling to obtain long-range and non-local information. Specifically, the feature extraction unit consists of a dense sequence of blocks, where the MPU converts the input into a fixed number of features, uses a feature-based kNN to group the features, refines each grouped feature through a tightly connected multilayer perceptron (MLP) chain, and finally obtains point features through maxpooling. The MPU introduces dense connections within and between blocks. This connection style enables explicit information reuse, which improves the reconstruction accuracy while significantly reducing the model size. This feature extraction method has applications in PU-GAN [13] and PU-EVA [31]. GC-PCU [32] simplifies this feature extraction method into a shallow-and-wide structure; only two extraction blocks are involved, and the number of channels is increased before activation. [PERSON] et al. [14] proposed a graph convolutional network-based point cloud upsampling model, PU-GCN, which is a new Inception DenseGCN feature extractor, and integrates the densely connected GCN (DenseGCN) module from DeepGCNs [33] into the Inception module of GoogLeNet [34]. Inception DenseGCN first compresses features through a set of MLPs to reduce the amount of computation, then passes the compressed features into two parallel DenseGCNs and a global pooling layer, and finally splices to obtain multi-scale feature information. AR-GCN [20] also uses graph convolution blocks to extract features.