LAUSR

The 3-D point cloud is a common 3-D data representation that has received increasing attention for remote sensing applications. However, processing 3-D point cloud semantics, especially local semantic information, has always been a challenge and has attracted much attention. In this article, we propose a novel enhanced local semantic learning transformer for 3-D point cloud analysis, which aims to enhance the transformer awareness of local semantic features to handle complex point cloud tasks. First, we propose a novel transformer framework, the local semantic learning point cloud transformer (LSLPCT), which not only learns 3-D point clouds of global information, but also enhances the perception of local semantic information end-to-end. Second, we design an efficient local semantic learning self-attention mechanism, namely, LSL-SA, which can parallelize the perception of global contextual information and capture finer grained local semantic features. Third, our proposed LSL-SA is easy to implement and can integrate the existing transformers and convolutional neural network (CNN)-based networks for processing various point cloud tasks. Numerous experiments in different types of point cloud tasks have been conducted, and our method performs better or is competitive with other state-of-the-art methods.