APPLICATION OF A SHELLNET BASED APPROACH TO SEMANTIC SEGMENTATION IN URBAN POINT CLOUD

Abstract

In recent years, the popularity of airborne, vehicle-borne, and terrestrial 3D laser scanners has driven the rapid development of 3D point cloud processing methods. The 3D laser scanning technology has the characteristics of non-contact, high density, high accuracy, and digitalization, which can achieve comprehensive and fast 3D scanning of urban point clouds. To address the current situation that it is difficult to accurately segment urban point clouds in complex scenes from 3D laser scanned point clouds, a technical process for accurate and fast semantic segmentation of urban point clouds is proposed. In this study, the point clouds are first denoised, then the samples are annotated and sample sets are created based on the point cloud features of the category targets using CloudCompare software, followed by an end-to-end trainable optimization network-ShellNet, to train the urban point cloud samples, and finally, the models are evaluated on a test set. The method achieved IoU metrics of 89.83% and 73.74% for semantic segmentation of buildings and rods-like objects respectively. From the visualization results of the test set, the algorithm is feasible and robust, providing a new idea and method for semantic segmentation of large-scale urban scenes.