An Approach for RGB-Guided Dense 3D Displacement Estimation in TLS-Based Geomonitoring

Abstract. Estimating 3D deformation with high spatial resolution from TLS point clouds is beneficial for geomonitoring. Existing methods for this task primarily rely on geometric data. They do not use radiometric information although it is often available as well. This leaves potential for improvement. To address this, we propose an approach that utilizes RGB images—captured by built-in cameras of TLS scanners and co-registered with TLS point clouds—to generate dense 3D displacement vector fields for deformation analysis. Our method comprises three main steps: (1) applying the Efficient-LoFTR algorithm to establish dense 2D pixel correspondences on RGB images across two epochs; (2) projecting 3D points from both epochs onto RGB images and establishing 3D point correspondences by matching the projected pixels with the established 2D correspondences; (3) clustering the point cloud of one epoch and refining the 3D point correspondences within each cluster to produce the final displacement vector fields. Experiments on real measurements obtained from a rockfall simulator and from a real-world landslide demonstrate that our method achieves comparable accuracy to state-of-the-art geometry-based methods, with improved density and computational efficiency. By using radiometric features, our approach complements geometry-based methods, suggesting that combining both will enhance coverage and/or accuracy for geomonitoring applications.