# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 2 Related Work  
**Three-Dimensional Shape Completion Methods Based on Deep Learning**: Deep learning technologies have made significant strides in the field of 3D shape completion, largely driven by neural networks and extensive training datasets. These advancements primarily focus on two types of data representation: voxels and point clouds.  
Voxelization-Based Shape Completion: Two-dimensional convolution techniques inspire the voxel representation method. For instance, the VConv-DAE network, proposed by [PERSON] et al. [18], is the first unsupervised learning model for 3D shape feature extraction. However, the process of converting point clouds into voxels inevitably leads to information loss, prompting researchers to explore direct point cloud data processing. [PERSON] et al.'s FoldingNet [19] demonstrated the feasibility of restoring high-dimensional features to point clouds. However, it generates complete point clouds through global encoding and fails to explicitly model local geometric features (such as surface curvature and edge sharpness), resulting in blurry details in the reconstruction. [PERSON] et al. [20] introduced TopNet, which treats point cloud generation as a tree growth process and designs a hierarchical decoder. [PERSON] and [PERSON]'s SA-Net [21] integrates a self-attention mechanism to improve the retention of local features, while [PERSON] et al.'s SoftPoolNet [22] replaces traditional max pooling with SoftPool to preserve more fine-grained details. [PERSON] et al. [23] proposed SnowflakeNet, which likens point cloud generation to snowflake-like growth, enhancing the richness of the output point cloud details. However, the use of only a global attention mechanism leads to the neglect of the correlation of local details in the final result. As 3D point cloud completion technology continues to evolve, progress has been made in transitioning from voxel-based processing to direct point cloud handling. Innovative network architectures have played a key role in significantly improving the performance and accuracy of shape completion tasks.  
**Point Transformer Attention Mechanism**: The attention mechanism has emerged as a powerful tool in neural networks, with its influence extending across various domains [24]. In the realm of point cloud data processing, this mechanism has opened up new possibilities. It is adept at calculating key relational information, such as the spatial distance and relative positions between individual points in the point cloud. Recently, the Transformer architecture has gained significant traction in the field of visual processing. Notably, the introduction of the Vision Transformer (ViT) model [25] has taken the application of the attention mechanism to new heights. In the context of 3D point cloud data, the Transformer architecture has demonstrated exceptional potential. [PERSON] et al. [10] and [PERSON] et al. [26] have each incorporated Transformer architecture into point cloud processing, designing innovative biased and self-attention mechanisms for feature extraction. The Transformer architecture has not only effectively captured the spatial relationships within point cloud data but also achieved significant results in tasks such as point cloud object classification and scene segmentation. Therefore, the application of the Transformer architecture has advanced 3D point cloud processing technology and provided strong support for practical applications in related fields. Most importantly, it has provided an effective approach for the point cloud completion work presented in this paper.