# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 2 Related Work
### Point-Based Methods  
#### 2.3.1 Graph-Based Methods  
Point cloud data, which are characterized by an irregular and a disordered distributions of points, inherently lack explicit interconnections among individual points. Nevertheless, these non-Euclidean geometric relationships can be effectively modeled through graph structures. PointNet [7] stands out as the pioneering network specifically designed for the direct processing of point clouds. Despite the groundbreaking achievements of the PointNet network in tasks such as point cloud classification or segmentation, it remains afflicted by the limitation of its inadequate capture of local neighborhood information. To address this limitation and extract more nuanced local features, [PERSON] [8] extended PointNet by introducing the PointNet++ network framework. The fundamental concept involves the construction of a local hierarchical module within the network. Each layer within this module comprises a sampling layer, a grouping layer, and a feature extraction layer. By selecting the local neighborhood center of mass through the _FPS_ layer--forming a local neighborhood subset via the k-NN layer--and deriving local neighborhood feature vectors through the PointNet layer, the framework adeptly captures local features across a multi-level hierarchical structure. Nonetheless, PointNet++ faces challenges due to its isolation of individual point sample features within the local neighborhood and the adoption of a greedy max pooling strategy for feature aggregation, thereby risking information loss and presenting certain constraints. In response to these issues, [PERSON] [14] proposed Softpool-net, which introduces the concept of soft pooling by substituting max pooling with a soft pooling mode. Unlike the exclusive retention of maximal features in max operation, soft pooling retains more features by preserving the first N maximal features during pooling. Meanwhile, [PERSON] [15] introduced 3D point cloud capsule networks, and they created an autoencoder tailored for processing sparse 3D point clouds while preserving spatial alignment and consolidating the outcomes of multiple maximal pool feature mappings into an informative latent representation through unsupervised dynamic routing. Nevertheless, it fails to adequately handle neighborhood points information, thus resulting in inadequate interactions between points. In order to enhance direct information exchange and foster better communication, DGCNN [16] employs the k-NN algorithm to construct local graphs, thus grouping points in semantic space and facilitating global feature extraction through continuous feature updates of edges and points. Notably, this approach enables the capture of geometric features of the local neighborhood while maintaining permutation invariance. Furthermore, DeepGCN [17] leverages deep Convolutional Neural Network (CNN) principles emphasizing deep residual connections, extended convolution, and dense connections, thereby enabling reliable training in deep models. GACNet [18], on the other hand, enhances segmentation results in edge areas by constructing a graphstructure for each point based on its neighboring points and integrating an attention mechanism to compute edge weights between the central point and its neighbors. [PERSON] [19] proposed a method for training deformed convolution kernels in local feature extraction, wherein an anchor point is initially selected, followed by the selection of neighboring points through k-NN. Subsequently, a set of displacement vectors is constructed to represent features in this region, thereby facilitating continuous updates of these displacement vectors to extract local point cloud features. Finally, multiple sets of learned displacement vectors are weighted and summed to construct the convolutional kernel for feature extraction, which is then applied to perform feature extraction on the image.