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

• URL: http://arxiv.org/abs/2506.16265v1
• Date: Thu, 19 Jun 2025 12:28:09 GMT
• Title: Dense 3D Displacement Estimation for Landslide Monitoring via Fusion of TLS Point Clouds and Embedded RGB Images
• Authors: Zhaoyi Wang, Jemil Avers Butt, Shengyu Huang, Tomislav Medic, Andreas Wieser,
• Abstract summary: Landslide monitoring is essential for understanding geohazards and mitigating associated risks.<n>Existing point cloud-based methods typically rely on either geometric or radiometric information.<n>We propose a hierarchical partition-based coarse-to-fine approach that fuses 3D point clouds and co-registered RGB images.
• Score: 7.144866519844918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Landslide monitoring is essential for understanding geohazards and mitigating associated risks. However, existing point cloud-based methods typically rely on either geometric or radiometric information and often yield sparse or non-3D displacement estimates. In this paper, we propose a hierarchical partition-based coarse-to-fine approach that fuses 3D point clouds and co-registered RGB images to estimate dense 3D displacement vector fields. We construct patch-level matches using both 3D geometry and 2D image features. These matches are refined via geometric consistency checks, followed by rigid transformation estimation per match. Experimental results on two real-world landslide datasets demonstrate that our method produces 3D displacement estimates with high spatial coverage (79% and 97%) and high 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. These values are below the average scan resolutions (0.08 m and 0.30 m). Our method outperforms the state-of-the-art method F2S3 in spatial coverage while maintaining comparable accuracy. Our approach offers a practical and adaptable solution for TLS-based landslide monitoring and is extensible to other types of point clouds and monitoring tasks. Our example data and source code are publicly available at https://github.com/zhaoyiww/fusion4landslide.

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