S^(2)ANet:Combining local spectral and spatial point grouping for point cloud processing  

作　　者：Yujie LIU Xiaorui SUN Wenbin SHAO Yafu YUAN 

机构地区：[1]College Department of Computer Science and Technology,China University of Petroleum(East China),Qingdao 266580,China

出　　处：《虚拟现实与智能硬件（中英文）》2024年第4期267-279,共13页Virtual Reality & Intelligent Hardware

摘　　要：Background Despite the recent progress in 3D point cloud processing using deep convolutional neural networks,the inability to extract local features remains a challenging problem.In addition,existing methods consider only the spatial domain in the feature extraction process.Methods In this paper,we propose a spectral and spatial aggregation convolutional network(S^(2)ANet),which combines spectral and spatial features for point cloud processing.First,we calculate the local frequency of the point cloud in the spectral domain.Then,we use the local frequency to group points and provide a spectral aggregation convolution module to extract the features of the points grouped by the local frequency.We simultaneously extract the local features in the spatial domain to supplement the final features.Results S^(2)ANet was applied in several point cloud analysis tasks;it achieved stateof-the-art classification accuracies of 93.8%,88.0%,and 83.1%on the ModelNet40,ShapeNetCore,and ScanObjectNN datasets,respectively.For indoor scene segmentation,training and testing were performed on the S3DIS dataset,and the mean intersection over union was 62.4%.Conclusions The proposed S^(2)ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.

关 键 词：Local frequency Spectral and spatial aggregation convolution Spectral group convolution Point cloud representation learning Graph convolutional network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]