Conference Agenda

Terrestrial laser scanning (TLS) enables dense spatial sampling; however, millimeter-level deformation analysis is limited by uncertainty rather than resolution, as inter-epoch differences can arise from actual change or residual systematic effects. Classical methods capture random variability under distributional assumptions but do not guarantee bounds for persistent systematic effects.

This paper presents a complementary interval-based framework that provides reliable, distribution-free bounds for TLS uncertainty and integrates seamlessly with least-squares workflows.

Starting from a measurement and instrumental correction model for high-end panoramic scanners, deviations of effective parameters are propagated to TLS observations and represented as interval radii at the observation level. We then extended the Least-Squares Adjustment, which linearly maps observation-level interval bounds to residuals and parameter estimates, providing conservative first-order enclosures alongside stochastic covariances.

Validation without a trusted nominal is addressed via a residual-based strategy that exploits two-face (Face 1/Face 2) acquisitions. This paper proposes a framework to validate intervals without existing nominal values. It begins with challenges and also guides addressing these challenges to ensure fair validation and test the proposed method on real TLS data. Overall, the proposed framework provides guaranteed bounds for remaining effects, improves discrimination between actual deformation and systematic effects, and offers actionable diagnostics for TLS-based monitoring.

The safety and longevity of civil infrastructure rely on robust structural health monitoring (SHM), yet conventional methods are

often constrained by the high cost and impracticality of contact-based sensors. On the other hand, existing non-contact technologies

typically specialize in either static geometric mapping or spatially limited dynamic vibration analysis, leading to fragmented data

and complex post-processing. This research presents a unified non-contact methodology that addresses this challenge by simul-

taneously acquiring high-resolution 3D geometry time-series vibrational data using a single Light Detection and Ranging (LiDAR)

device. For this purpose, we compare point-based measurements using a total station, an iPhone along with a profile-based LiDAR

and 3D LiDAR point clouds for an experimental analysis. Sensor observations are recorded and analyzed at the same location

on the experimental surface showing flexibility in input dimensionality as well as robustness in resulting scalograms. The core

of the analysis is our developed method, a directional wavelet transform, a signal processing technique uniquely suited handling

non-stationary signals as multidimensional unstructured data. This method enables the characterization of oscillations across the

unstructured 3D surface, a capability beyond traditional modal analysis with one-dimensional time-frequency localization, but using

LiDAR point cloud time series. The result is a richer and more integrated understanding of structural behavior, capable of revealing

vibration behavior in high spatial detail. The study demonstrates that spatio-temporal LiDAR data contains embedded dynamic

information, offering a more comprehensive and efficient way to assess the health and integrity of a structure in the future.

The quasi-continuous capturing of our environment by terrestrial laser scanning (TLS) in form of 3D point clouds provides the basis for numerous spatial analyses, including space-continuous deformation analysis. In times of aging infrastructure and climate

change-induced, cumulative mass movements, statistically-sound methods for determining areal deformations are becoming increasingly important. However, the lack of reproducibility of absolute point positions between consecutive scans and the resence of measurement noise demand approaches that retrieve credible comparison statements. The representation of point clouds by geometric surfaces supports noise reduction and serves as basis for successive analysis. Tensor-product B-spline surfaces have proven to be particularly versatile geometric representations to derive spatially consistent deformation estimates. This paper extends this concept by investigating the use of truncated hierarchical B-splines for statistically sound deformation analysis. We show that deformation is detectable when partition of unity is preserved through truncation. In a simulated environment, significant deformations between two point clouds were successfully detected. Results indicate that coarse surface representations lead to type-1 errors and underestimated deformation magnitudes, whereas more refined surface representations yield consistent deformation estimates, providing a potential termination criterion for adaptive model refinement.

Accurate and spatially dense 3D displacement estimation can contribute to a better understanding of geomorphological processes, while long-range terrestrial laser scanning (LR-TLS) has emerged as a promising technique for generating such observations. However, selecting the most effective algorithms for dense 3D displacement estimation remains challenging due to the lack of benchmarking. This study introduces an open and extensible benchmarking framework for 3D displacement estimation and provides an initial validation through a systematic comparison of representative 2D projection-based and 3D point cloud--based methods for estimating 3D displacements from LR-TLS scans. The evaluation includes 252 combinations of algorithmic and hyperparameter configurations, covering cross-correlation, optical flow, and salient feature tracking approaches, as well as the 3D displacement estimation method F2S3. All methods were benchmarked on a single common LR-TLS dataset, using sparse GNSS and manually derived displacements as ground truth. Results show that F2S3 achieves the highest agreement with the ground truth, while the top-performing configurations of the 2D approaches reach comparable accuracy, albeit slightly lower than that of F2S3. Our findings further highlight key sensitivities of current methods to parameter choices and data characteristics. The presented open and extensible evaluation framework enables reproducible performance assessment and could provide a foundation for future large-scale benchmarking and further development of 3D displacement estimation techniques for LR-TLS data.

Structural health monitoring of water dams is crucial to ensure their long-term safety and operational reliability. Traditional geodetic techniques, although precise, are limited to sparse observation points and cannot capture spatially heterogeneous deformations. Laser scanning enables comprehensive, area-wide acquisition, overcoming this limitation. Subsequent deformation analysis often relies on comparisons along the local surface normal, which are limited in detecting in-plane movements. To address this, this study presents an approach that combines image-based stone segmentation with point-cloud-based deformation analysis to estimate both in-plane and out-of-plane displacements across masonry dam surfaces. Individual stones are detected in unmanned aerial vehicle (UAV) imagery using a deep learning segmentation model (Mask R-CNN) and subsequently projected into corresponding point clouds acquired by terrestrial laser scanning (TLS) and UAV laser scanning. By establishing consistent stone correspondences across multi-epoch point clouds via centroid-based matching and local iterative closest point (ICP) alignment, the proposed method enables deformation analysis on a stone-by-stone level. Simulated deformations were applied to TLS- and UAV-based point clouds of a dam to evaluate the method. Results demonstrate that the approach achieves sub-centimeter accuracy for the TLS and low-centimeter accuracy for the UAV point cloud, as measured by the RMSE between the estimated and true deformation. Our approach outperforms conventional model-to-model comparison methods, such as Multiscale Model to Model Cloud Comparison (M3C2), for in-plane displacements. The integration of image segmentation and geometric analysis provides a powerful framework for full-field deformation monitoring of masonry structures, supporting the detection of instabilities and improving dam safety.

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.

Using point clouds captured by Terrestrial Laser Scanners for measurement tasks with high-quality requirements is well established in engineering geodesy. However, geometric imperfections within the scanners introduce systematic deviations into the captured point clouds. These deviations often reach several millimeters in magnitude, exceeding the impact of random measurement noise. Calibrating the scanners by estimating these internal imperfections allows these systematic errors to be corrected, thereby preventing misinterpretations of the measurement results. In this work, we develop a methodology that allows users of Terrestrial Laser Scanners to independently determine calibration parameters for panorama scanners and to correct the resulting point clouds using planar patches extracted directly from the captured data. This approach requires no additional hardware or specialized measurement equipment. We evaluate the methodology using an independent point cloud of a water dam and demonstrate that it achieves a substantial reduction in systematic deviations. Furthermore, by estimating calibration parameters in a dedicated state-of-the-art calibration field, we show that our method delivers results comparable to these established calibration procedures—yet without the need for such specialized calibration environments.

Information on the precision of TLS observables is limited. While the range measurement precision can be modeled with respect to the intensity measurement nowadays, the precision of the angular observations still relies on the claims of the manufacturer. This contribution proposes a method to determine the vertical angular variance of a TLS using profile measurements. Supported by a simulation, which serves as proof-of concept, the methodology is laid out. In the end, measurements with a Z+F IMAGER® 5016A are evaluated. A dependency of the angular standard deviation on the rotational speed of the beam deflection unit is observed. The estimation precision of the angular standard deviation is high with consistent values for differing ranges. The estimated angular standard deviations are much lower than the claims of the manufacturer starting with roughly 2" for the slowest rotating settings, up to 4" for the fastest. All this can be achieved by scanning a reflectivity target with at least two adjacent fields of different homogeneous reflectivity. This needs to be aligned to the scanner to reduce and eliminate as many contributing error sources as possible. The target itself provides the fields and the transitions needed to perform the in-situ estimation of the angular precision.