# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
A hypergraph neural network is a neural network structure that utilizes higher-order data correlations for representation learning. Compared to graph neural networks, it is better able to capture both global and local information in data. Currently, hypergraph neural networks have shown excellent performance in a variety of tasks such as object retrieval and classification [16], action recognition [17], sentiment prediction [18] and recommender systems [19]. In the literature [20; 21; 22; 23], K-Nearest Neighbor strategy is used to construct hypergraphs, and a hyperedge convolution operator is proposed to obtain the output features of the vertices by aggregating hyperedge features in which the vertices are located. The literature [19; 21; 24] considers the attention of the node layer and the hyperedge layer and introduces the attention mechanism into the vertex convolution and hyperedge convolution process, which automatically learns the different weights of the vertices and hyperedges during the feature transformation and propagation process. Although hypergraph neural networks have shown significant advantages in a variety of tasks, there are still some challenges in applying them to point cloud semantic segmentation. Firstly, in the current hypergraph neural networks, the widely adopted hyperedge convolution operator [20; 25] can effectively aggregate the local information of the nodes in the hypergraph, but in the face of discrete and disordered point cloud data, it is difficult to capture the correlations between local and global features in the data, which leads to the incompleteness and distortion of the information. Secondly, some current hypergraph attention operators [26; 27] adopt the vertex-hyperedge-vertex feature transformation mode by using hyperedges as the intermediate layer, but this practice increases the number of parameters, leading to an increase in model complexity. These limitations make it difficult for the current hypergraph neural networks to process large-scale point cloud data. Therefore, it is urgent to design more suitable hypergraph neural network structures for point cloud data processing, thereby better improving the semantic segmentation performance of point clouds. To this end, we propose an end-to-end hypergraph deep learning framework, i.e., hypergraph position attention convolution network framework, for semantic segmentation of point clouds. Specifically, in order to efficiently organize disordered, unstructured, and high-dimensional point clouds, we construct a hypergraph to capture correlations between point clouds by combining the farthest point sampling and ball query methods. Then, we propose a hyperedge position attention convolution operator for extracting high-level semantic features of point clouds. This operator adopts the hyperedge-hyperedge feature propagation model, which not only effectively utilizes the spatial positional information and higher-order information of the point cloud but also avoids the vertex to hyperedge propagation process, reducing the number of network parameters. Finally, we design a ResNet-like module for feature learning, which further improves the efficiency of the network by introducing deep convolution into the network. The main contributions of our work are summarized as follows:  
1. We propose a new hyperedge position attention convolution module for feature information extraction, which makes the network focus more on task-related feature information through the position information of the points and the combination of hyperedges generated from other features. 2. We design a hypergraph position attention convolution network framework for the semantic segmentation of point clouds. Particularly, we introduced a ResNet-like deep convolution module to lighten the network and improve its efficiency. 3. We perform segmentation and a series of ablation experiments on the S3 IDS and ShapeNet Part datasets to validate the performance of the proposed method.