RFNet: Convolutional Neural Network for 3D Point Cloud Classification

Due to the unstructured and disordered nature of point cloud, the classification accuracy is still needed to improve for the currently existed point cloud classification approaches. In this paper, an effective point cloud classification network is proposed by considering local structure construction, global feature aggregation, and loss function improvement. Firstly, in view of the unstructured nature of point cloud, the local structure is constructed via assigning different weights to those irregular neighborhood points, which are computed via the relationship between the center point feature and its neighbor feature. Moreover, a strategy of weighted average pooling (WAP) is designed to learn the attention score of each high-dimensional feature through self-attention mechanization, which can effectively aggregate redundant high-dimensional features while coping with point cloud disorder. In addition, a joint loss function (JL) is presented by utilizing the complementary relationship between the cross-entropy loss and the center loss, which increases the inter-class distance while reduces the intro-class distance, and further improves the classification ability of the network. Compared to several state-of-the-art networks, the experimental results on the synthetic dataset ModelNet40 and ShapeNetCore and the real-world dataset ScanObjectNN demonstrate the superiority of the overall network structure. © 2023 Science Press. All rights reserved.