In geodetic monitoring, terrestrial laser scanning (TLS) point clouds are typically assumed to be accurate and true-to-scale, implying that data acquired from different epochs or stations differ only by rigid transformations. Consequently, systematic errors related to scanner or platform variations can be mitigated through rigid point cloud registration. However, variations in the propagation speed and path of laser beams due to atmospheric refraction, as well as ranging biases induced by surface properties, can introduce non-rigid distortions in the generated point clouds. These effects are particularly pronounced under complex meteorological and topographic conditions, such as in mountainous areas. As a result, the acquired point clouds exhibit inhomogeneous and non-linear deviations that cannot be effectively compensated by simple distance corrections or rigid transformations. In this study, robust rigid registration is first performed to minimize the effects of platform offsets. A data-driven approach is then employed to generate sparse stable points, providing distance deviations that incorporate spatially varying systematic errors. Finally, a free-form surface is fitted to these sparse point-wise distance deviations, thereby establishing a 3D correction field for the entire point cloud. For a dataset collected by a permanent TLS monitoring system in the Vals Valley (Tyrol, Austria), the proposed method effectively reduces the registration residuals in TLS point clouds caused by inhomogeneous systematic errors.