SCA-Net: Spatial and channel attention-based network for 3D point clouds

Abstract

A key challenge in data processing of 3D point cloud data is how to identify key regions in the point cloud from geometric relationships. Previous work has attempted to overcome this deficiency by slicing and dicing data. Such an approach limits the model’s ability to effectively learn global information. In this paper, in order to better extract features and obtain geometric information we propose a Point Attention (PointAT) model and propose Attention Value (AT value) model for feature fusion to apply geometric relationship to the data. Then, we propose a new Spatial and Channel Attention-based network (SCA). The SCA is the overall structure of the network, and the main purpose is to connect PointAT and AT value model, then capturing meaningful geometric information by applying the geometric relationship between point clouds patches to the model, then propose a auto pooling framework to extract to global features. In this work, we concentrate on learning geometric relationship between point cloud data. For this purpose, we introduce a point attention model based on spatial and channel attention to learn the geometric relationship between point clouds, and further combine the geometric relationship with the point cloud data by the AT Value Model. Finally, we introduce a adaptive downsampling structure, Autopooling. This downsampling structure consider each point’s importance weight and picking key points adaptively, which can be used with convolutional networks. Extensive experiments conducted on two benchmark datasets (ModelNet40 and ShapeNet) clearly demonstrate the effectiveness of our SCA and SCA-Auto (SCAA with Auto pooling) methods.