Research on Transformer Point Cloud Segmentation Technology under Self Attention Mechanism

Abstract

Point cloud data has a wide range of applications in fields such as computer vision, robotics, and autonomous driving. However, the disorderliness and irregularity of point clouds make effective segmentation a challenging problem. The traditional point cloud segmentation methods have certain limitations, while the Transformer model based on self attention mechanism brings new ideas and technical means for point cloud segmentation. This article delves into the Transformer point cloud segmentation technology based on self attention mechanism, elaborating on its basic principles and key technologies. Firstly, the basic concepts of point cloud data and segmentation tasks were introduced, followed by a detailed analysis of the advantages and challenges of Transformer architecture and self attention mechanism in point cloud segmentation. Then, the construction process of Transformer point cloud segmentation model based on self attention mechanism was discussed, including network structure design, feature extraction, coordinate encoding, and other aspects of the model. The performance of this technology in point cloud segmentation tasks was compared with traditional methods and other deep learning based methods through experiments, and the results showed that the Transformer model based on self attention mechanism has higher segmentation accuracy.