Dense 3D Displacement Estimation for Landslide Monitoring via Fusion of TLS Point Clouds and Embedded RGB Images

TL;DR

This paper introduces a hierarchical method that fuses 3D point clouds and RGB images to produce dense, accurate 3D displacement fields for landslide monitoring, improving coverage over existing techniques.

Contribution

It presents a novel hierarchical coarse-to-fine approach integrating geometric and radiometric data for dense 3D displacement estimation in landslides.

Findings

Achieves 79% and 97% spatial coverage on real datasets.

Displacement accuracy within 0.15 m and 0.25 m compared to external measurements.

Outperforms state-of-the-art F2S3 in coverage while maintaining accuracy.

Abstract

Landslide monitoring is essential for understanding geohazards and mitigating associated risks. Existing point cloud-based methods, however, typically rely on either geometric or radiometric information and often yield sparse or non-3D displacement estimates. In this paper, we propose a hierarchical partitioning-based coarse-to-fine approach that integrates 3D point clouds and co-registered RGB images to estimate dense 3D displacement vector fields. Patch-level matches are constructed using both 3D geometry and 2D image features, refined via geometric consistency checks, and followed by rigid transformation estimation per match. Experimental results on two real-world landslide datasets demonstrate that the proposed method produces 3D displacement estimates with high spatial coverage (79% and 97%) and accuracy. Deviations in displacement magnitude with respect to external measurements (total station or GNSS observations) are 0.15 m and 0.25 m on the two datasets, respectively, and only 0.07 m and 0.20 m compared to manually derived references, all below the mean scan resolutions (0.08 m and 0.30 m). Compared with the state-of-the-art method F2S3, the proposed approach improves spatial coverage while maintaining comparable accuracy. The proposed approach offers a practical and adaptable solution for TLS-based landslide monitoring and is extensible to other types of point clouds and monitoring tasks. The example data and source code are publicly available at https://github.com/gseg-ethz/fusion4landslide.