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Multi-temporal acquisitions of 3D point clouds for geomonitoring tasks allow the quantification and analysis of geometric changes of monitored objects by advanced processing algorithms, further revealing the underlying deformation mechanism. Among numerous approaches proposed in the geoscientific domain for point cloud-based deformation analysis, m...
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... Hence, these approaches inevitably capture only a projection of the full 3D displacement, resulting in underestimation or omission of significant displacements. Even some state-of-the-art M3C2 variants (James et al., 2017;Zahs et al., 2022;Yang and Schwieger, 2023) do not fully resolve these issues, as they primarily focus on refining the quality, e.g., by local patch or plane-based filtering, rather than addressing the limitations in dense 3D displacement estimation. Consequently, these methods offer insight into the distribution of 1D displacements within 3D space but fail to capture the full extent of 3D displacement fields. ...
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.
... Specifically, a Gaussian filter was used, wherein each pixel's value was replaced by a weighted average of its neighbours, effectively minimizing small-scale outliers. Lastly, we employed the Multiscale Model to Model Cloud Comparison (M3C2) algorithm, which calculates the vertical component of normal distances between two ALS-derived point clouds along a locally derived surface normal (Yang and Schwieger, 2023). By iteratively selecting a neighbourhood around each point and projecting it along the normal, M3C2 quantifies both the mean vertical displacement and its associated uncertainty, producing a raster of surface changes. ...
... However, the analysis covered only half of the structure, and the results were unsatisfactory because the measured shell thickness was less than the values in design documents [8]. The literature offers extensive discussions on the problem of the limited reliability of distances between TLS data computed using the C2C method, which is a sum of the actual distance, measurement noise, and systematic errors linked mainly to the registration, georeferencing, and stability of the reference frame [32,[54][55][56][57][58]. Distance underestimation due to the juxtaposition of raw point clouds [59] calls for diverse methods for extracting or interpolating measurement data [60]. ...
... One solution that offers complete 3D point cloud comparisons, using TLS observation redundancy, a simple and high-performance alternative to reliable methods requiring detailed TIN models, is M3C2 (multiscale model-to-model cloud comparison) [53]. The M3C2 algorithm developed by Lague et al. [53], where the distances between two point clouds along locally determined normals to the investigated surfaces are computed, is typically used in geomonitoring [58,63,[65][66][67]. M3C2-based solutions for differentiating point clouds while factoring in measurement uncertainty sources are also successfully employed in civil engineering diagnostics [57,68,69], comparing data from various measurement methods [70,71], and analysing the variability among point clouds acquired in different measurement conditions [72]. ...
... The results demonstrated the usability of the approach for identifying expanding defects and developing a structural renovation strategy, which is necessary for proper object management. M3C2's capabilities regarding point cloud differentiation are confirmed by its various adaptations to specific circumstances [58,67,73]. The most common one, M3C2-EP, expands the estimation of the 'level of detection' (LoDetection, defined by Lague et al. [53]) with error propagation, which integrates internal measurement uncertainties and ununiform registration uncertainties to yield more reliable results for rough surfaces [67]. ...
Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional curvature continuity, makes them stand out against other towers and poses very high construction and service requirements. The safe service and adequate durability of the hyperboloid structure are guaranteed by the proper geometric parameters of the reinforced-concrete shell and monitoring of their condition over time. This article presents an original concept for employing terrestrial laser scanning to conduct an end-to-end assessment of the geometric condition of a hyperboloid cooling tower as required by industry standards. The novelty of the proposed solution lies in the use of measurements of the interior of the structure to determine the actual thickness of the hyperboloid shell, which is generally disregarded in geometric measurements of such objects. The proposal involves several strategies and procedures for a reliable verification of the structure’s verticality, the detection of signs of ovalisation of the shell, the estimation of the parameters of the structure’s theoretical model, and the analysis of the distribution of the thickness and geometric imperfections of the reinforced-concrete shell. The idea behind the method for determining the actual thickness of the shell (including its variation due to repairs and reinforcement operations), which is generally disregarded when measuring the geometry of such structures, is to estimate the distance between point clouds of the internal and external surfaces of the structure using the M3C2 algorithm principle. As a particularly dangerous geometric anomaly of hyperboloid cooling towers, shell ovalisation is detected with an innovative analysis of the bimodality of the frequency distribution of radial deviations in horizontal cross-sections. The concept of a complete assessment of the geometry of a hyperboloid cooling tower was devised and validated using three measurement series of a structure that has been continuously in service for fifty years. The results are consistent with data found in design and service documents. We identified a permanent tilt of the structure’s axis to the northeast and geometric imperfections of the hyperboloid shell from −0.125 m to +0.136 m. The results also demonstrated no advancing deformation of the hyperboloid shell over a two-year research period, which is vital for its further use.
... A special case of point-cloud-based monitoring involves subdividing the point cloud into tiles or even using only selected patches [28][29][30]. In this case, a rigid body movement is calculated individually for each point cloud patch, for example, using the iterative closest point (ICP) algorithm [31]. ...
In this paper we propose a monitoring method that allows the inclusion of point clouds into a geodetic monitoring network. Consequently, network adjustment and a rigorous deformation analysis can be performed allowing consistent error propagation and trustful significance calculation. We introduce a supervised pipeline based on ICP-matching of small-scale laser scan patches. It is specially designed for geo-monitoring applications and allows to improve the spatial resolution as well as the network geometry of monitoring networks in inaccessible hazardous areas in the mountains, where the installation of a sufficient number of targets is difficult. We apply our method to two datasets. The first is a monitoring setup in the laboratory, where we establish the parameters for the supervised patch selection and demonstrate how the network geometry is improved. Second is the real case study of Mt. Hochvogel, where the proposed method helps to clearly improve the spatial resolution of deformation vectors.
