Attentive Enhanced Convolutional Neural Network for Point Cloud Analysis

Although methods based on deep learning have achieved remarkable success in the field of point cloud analysis, the recognition accuracy of point cloud analysis is still far from practical applications. In this paper, we propose a novel end-toend attentive enhanced convolutional neural network for point cloud analysis, named AECNN, which can analyze point clouds with high accuracy. The key component in our method is the attentive enhanced convolution (AEConv) module. It designs an attention mechanism to enhance the features of each group through the interaction of information between groups, so that the feature expression is more sufficient and richer. Based on AEConv, we further design an attentive enhanced convolutional neural network (AECNN) for 3D model classification and segmentation. AECNN continuously abstracts the entire point cloud by stacking downsampling and AEConv to obtain feature descriptors that can represent the entire point cloud, and finally uses a classification head and a segmentation head to complete the task of point cloud classification and segmentation, respectively. Experiments are conducted on the point cloud recognition and large-scale point cloud outdoor segmentation datasets, i.e., ModelNet40 and vKITTI, respectively. Extensive experiments show that our AECNN can achieve high performance for classification and segmentation tasks, which exceeds previous point cloud recognition and segmentation algorithms. © 2023, IAENG International Journal of Computer Science. All Rights Reserved.