# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
## 2 Related Works  
**Encoding and learning of local features.** [PERSON] et al. [6] pioneered the field of point cloud upsampling with their PU-Net, a deep learning approach that leverages PointNet++ [13] for feature enhancement and a fully connected layer for reconstructing upscaled coordinates. [PERSON] et al. [8] introduced a novel approach by representing 3D surfaces using 2D parameter spaces to enrich point data. In their subsequent work, PU-GCN [14], they employed graph convolutional networks (GCNs) in combination with shared multilayer perceptrons (MLPs) and parallel local feature learning networks, effectively processing multiscale features. [PERSON] et al. [15] integrated the self-attention mechanism into point cloud upsampling, emphasizing the enhancement of local feature distinguishability. [PERSON] et al. [16] took a different approach, learning 2D perturbations through MLPs to estimate coordinate offsets between input and upsampling points. They further refined these offsets through residual learning, combining extracted features with the learned 2D perturbations. More recently, [PERSON] et al. [9] introduced Grad-PU, an algorithm that seamlessly combines mid point interpolation in Euclidean space with iterative optimization of distances to the target high-resolution point cloud.  
These advancements collectively highlight the critical role of local feature learning in point cloud upsampling. They have significantly improved the quality of upsampling results. However, a persistent challenge remains feature homogenization, which results in the generated points being too close or far apart and blurring of the sharper points in the crucial regions. Future research efforts should focus on addressing this challenge to further enhance the reliability and accuracy of point cloud upsampling techniques.  
**Upsampling network architectures.** In the realm of point cloud upsampling, network architectures can be broadly classified into two categories: supervised and unsupervised (or self-supervised) networks. Supervised networks leverage metrics such as point uniformity [17], point-to-point distance [6], and reconstruction loss [18] to meticulously refine dense point clouds by comparing them to ground truth (GT) data. [PERSON] et al. [7] introduce Dis-PU, a two-phase model that combines feature expansion and spatial refinement, delivering high-quality, uniformly distributed point clouds. On the other hand, unsupervised methods, exemplified by GAN networks [17] and the self-supervised SPU-Net [19], focus on learning directly from the inherent structure of the point cloud, without relying on GT comparisons. These methods underscore the significance of feature supervision in network design, particularly in self-supervised learning, for enhancing upsampling quality.  
However, a notable limitation of these methods is their heavy reliance on reconstruction-based supervised learning. As the quality of high-dimensional features deteriorates, their effectiveness in supervising upsampling decreases. Additionally, these methods often overlook the supervision of the intrinsic local structure within point clouds, which is crucial for accurate and detailed upsampling. Future research should aim to address these challenges, exploring novel supervision strategies that can better capture and preserve the local structure of point clouds, leading to more robust and accurate upsampling results.