Feature processing of point cloud data is a key component of 3D object recognition technology in robotics, autopilot, and other fields. In order to address the problems of repeated extractions of local feature information of point cloud and lack of recognition of the whole geometric structure of point cloud object, a point cloud classification and segmentation network based on ring query and channel attention was proposed. First the single-layer ring query was combined with the feature channel attention mechanism to reduce local information redundancy and strengthen local features. Then the high response points of the edges and corners of the object were identified by calculating the normal changes, and the normal features were added to the global feature representation, thereby strengthening the recognition of the whole geometric structure of the object. Compared with many point-cloud networks on ModelNet40 and ShapeNet Part datasets, the experimental results show that the network not only has higher accuracy for point cloud classification and segmentation, but also outperforms other methods in training time and memory consumption. In addition, the network is strongly robust for the number of different input point clouds. Therefore, the proposed network is an effective and feasible network for point cloud classification and segmentation.

point cloud data, classification and segmentation, ring query, channel attention, high response point recognition