Lightweight Attention Module for Deep Learning on Classification and Segmentation of 3-D Point Clouds

Abstract

Research on classification and segmentation of 3-D point clouds using deep learning methods has become a hot topic in emerging applications, such as autonomous driving, augmented reality, and indoor navigation. However, as the complexity of the network structures increases, the computational efficiency reduces, which affects the practical applications of these methods. In addition, prior researchers mostly seek to enhance the quality of spatial encodings, while the channel relationships are ignored. It makes the feature learning of point clouds insufficient, which will reduce the accuracy of classification and segmentation. In this article, a lightweight attention module (LAM) is proposed to improve the computational efficiency and accuracy at the same time by adopting a novel convolution mode and introducing a new attention mechanism based on channelwise statistical features. As the submodules of LAM, the lightweight module and the attention module can also be used independently to focus on improving the computational efficiency and accuracy, respectively, according to the actual applications. LAM and its submodules can be easily integrated into state-of-the-art deep learning methods on classification and segmentation of 3-D point clouds. The experimental results show that the proposed modules have a good performance on benchmark data sets.