# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation  
[PERSON]  
1 School of Electronic and Information Engineering/School of Integrated Circuits, Guangxi Normal University, Guilin 541004, China; [EMAIL_ADDRESS] (Y.R. ); [EMAIL_ADDRESS] (Z.H. ); [EMAIL_ADDRESS] (Y.H. );  
College of Physics and Technology, Guangxi Normal University, Guilin 541004, China; [EMAIL_ADDRESS] (C.R. );  
College of Computer Science and Engineering, Guangxi Normal University, Guilin 541004, China; [EMAIL_ADDRESS] (C.R. );  
College of Computer Science and Engineering, Guangxi Normal University, Guilin 541004, China; [EMAIL_ADDRESS] (C.R. );  
College of Computer Science and Engineering, Guangxi Normal University, Guilin 541004, China; [EMAIL_ADDRESS]. pointnet++ [5] process points independently on a local scale to maintain permutation invariance. However, this independence ignores the geometric relationships between points, resulting in an inability to capture local features [6; 7]. The graph convolution-based methods, such as GACNet [8] and HDGCN [9], model point clouds with graphs, then learn point cloud features using graph neural networks. The methods improve the performance of point cloud segmentation to a greater extent. However, they consider only pairwise relationships among data while ignoring higher-order relationships. The higher-order relationships refer to those between two or more objects [10]. A growing number of studies have shown that focusing on higher-order relationships can help to dig deeper into the potential connections between data samples, thus improving the capability of the model. Hypergraph is a generalized graph structure that extends the traditional notion of a graph. It is composed of a vertex set and a hyperedge set. The hyperedge set is a collection of subsets of the vertex set, and each hyperedge can connect one or more vertices [11]. This structure allows hypergraphs to represent and handle complex relationships more flexibly. Due to the advantages of hypergraph in modeling data correlation. In recent years, some scholars have adopted hypergraph tools to analyze and process point cloud data, but the related studies are quite sparse. [PERSON] et al. [12] introduced a tensor-based approach to estimate the hypergraph spectral components and frequency coefficients of the point cloud in both ideal and noisy environments, established an analytical relationship between hypergraph frequencies and structural features, then evaluated the effectiveness of hypergraph spectra in the tasks of point cloud sampling and denoising. Subsequently, they investigated the capability of hypergraph spectral analysis in unsupervised segmentation of 3D point clouds [13]. In addition, [PERSON] et al. [14] proposed a 3D object detection method for noisy point clouds based on hypergraph construction-compression-conversion based on the fact that the hypergraph is robust to noise, by constructing hypergraphs with multi-scale voxelized structures and clustering methods, then transforming hypergraphs into graphs, as well as learning the features using graph neural networks. [PERSON] et al. [15] investigated point cloud resampling based on hypergraph signal processing (HGSP) and designed hypergraph spectral filters to capture multilateral interactions between nodes. In the above methods, the method of utilizing hypergraph spectrum and hypergraph signal processing will bring the problem of excessive computational complexity, and the method of converting hypergraph to graph not only brings a conversion cost but also leads to loss of higher-order information. Therefore, it is necessary to apply deep learning techniques to the hypergraph, such as constructing hypergraph neural networks, to give full access to their ability in representation learning by utilizing higher-order data correlations, so as to comprehensively explore the potential information in the data and obtain better point cloud semantic segmentation performance.