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.

翻译：滑坡监测对于理解地质灾害和减轻相关风险至关重要。然而，现有的基于点云的方法通常仅依赖几何或辐射信息，且往往只能生成稀疏或非三维的位移估计。本文提出了一种基于层次划分的从粗到精的方法，该方法融合三维点云与配准的RGB图像来估计密集的三维位移矢量场。我们利用三维几何与二维图像特征构建面片级匹配，通过几何一致性检查进行精化，随后为每个匹配估计刚性变换。在两个真实滑坡数据集上的实验结果表明，所提方法能够生成具有高空间覆盖率（79%和97%）和高精度的三维位移估计。在两个数据集上，位移大小相对于外部测量（全站仪或GNSS观测）的偏差分别为0.15米和0.25米，与人工推导的参考值相比仅为0.07米和0.20米，均低于平均扫描分辨率（0.08米和0.30米）。与最先进的F2S3方法相比，所提方法在保持相当精度的同时提高了空间覆盖率。该方法为基于TLS的滑坡监测提供了一个实用且适应性强的解决方案，并可扩展至其他类型的点云和监测任务。示例数据与源代码公开于 https://github.com/gseg-ethz/fusion4landslide。