S2ANet: Combining local spectral and spatial point grouping for point cloud processing

Abstract

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.

Keywords

• Local frequency
• Spectral and spatial aggregation convolution
• Spectral group convolution
• Point cloud representation learning
• Graph convolutional network