首页|S2ANet:Combining local spectral and spatial point grouping for point cloud processing
S2ANet:Combining local spectral and spatial point grouping for point cloud processing
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
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(S2ANet),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 S2ANet was applied in several point cloud analysis tasks;it achieved state-of-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 S2ANet can effectively capture the local geometric information of point clouds,thereby improving accuracy on various tasks.
Local frequencySpectral and spatial aggregation convolutionSpectral group convolutionPoint cloud representation learningGraph convolutional network
Yujie LIU、Xiaorui SUN、Wenbin SHAO、Yafu YUAN
展开 >
College Department of Computer Science and Technology,China University of Petroleum(East China),Qingdao 266580,China
万方数据
