Digital reconstruction of railway steep slope from UAV+TLS using geometric transformer

Abstract

Accurate representation of railway slopes, especially those that are steep, is vital for real-time risk perception. Also, temporary structures also present certain safety hazards due to lack of monitoring. Traditional point cloud modeling, employing Unmanned Aerial Vehicle (UAV) or Terrestrial Laser Scanning (TLS), often struggles to simultaneously account for the precision of both surface and overhead models, leading to considerable model distortion, roughness, and deviation. Addressing these issues, A new 3D point cloud modeling algorithm for railway slopes based on a geometric transformer is presented in this paper. This involves an innovative rough point cloud denoising technique leveraging adaptive segmentation, multi-scale denoising, and deep learning point cloud registration. Our approach significantly enhances UAV point cloud accuracy and supplements missing portions of the TLS point cloud dataset occluded by objects block, using data from the UAV point cloud set. An experimental study shows that the score-based denoising algorithm improves precision from 37.44 mm to 8.11 mm for a UAV 3D point cloud. Further, by registering the UAV and TLS point cloud sets using the Geometric Transformer algorithm, the precision of the 3D point cloud was further augmented to 5.11 mm, representing a sevenfold enhancement over the initial UAV point cloud accuracy prior to denoising. Consequently, a high-fidelity 3D point cloud model of steep railway slopes has been created.