Nicholas Meyer


Last Name

Meyer

First Name

Nicholas

ORCID

0009-0004-8943-969X

Organisational unit

03964 - Wieser, Andreas / Wieser, Andreas

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20254
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Publications 1 - 4 of 4
  • Quantifying and Reducing the Uncertainty of 3D Displacement Estimates from Terrestrial Laser Scanner Point Clouds
    Item type: Conference Paper
    Shi, Nianfang; Medic, Tomislav; Meyer, Nicholas; et al. (2025)
    Terrestrial laser scanning (TLS) can offer an effective solution to geomonitoring problems by providing highresolution point clouds, which serve as a basis for estimating dense 3D displacements. The uncertainty of such estimates, as well as the means of reducing it remain largely unexplored. We present a case study to evaluate the accuracy of TLS-based deformation estimates from an alpine monitoring campaign consisting of 6 scanning epochs between 2019 and 2022. The point clouds acquired with a Riegl VZ-4000 scanner were processed using the Feature to Feature Supervoxel-based Spatial Smoothing (F2S3) algorithm to estimate the 3D displacement vectors. We compared these vectors to sparsely distributed ground truth measurements, acquired using Global Navigation Satellite System (GNSS) stations. The results showed that, if adequately spatially averaged over large areas, the 3D vectors can be estimated with an accuracy of a few centimeters despite the long distances of up to 4 km. This corresponds to an accuracy of a few centimeters for the displacement magnitude, and a few degrees for the direction (if the magnitude is large enough for a meaningful estimate of the direction). Herein, we additionally explore several strategies to reduce the uncertainty, where temporal averaging of multiple consecutive scans from a single epoch proved to be the most promising one, while vegetation filtering and a careful selection of the registration approach indicated limitations that require further investigations.
  • Investigation of different registration methods for TLS-based deformation analysis of hydroelectric dams – A case study
    Item type: Conference Paper
    Meyer, Nicholas; Medic, Tomislav; Friedli, Ephraim; et al. (2025)
    Proceedings - 6th JISDM 2025
    In this paper, we investigate various registration techniques for Terrestrial Laser Scanning (TLS) in the context of deformation analysis of hydroelectric dams. Accurate spatiotemporal registration of TLS data is particularly challenging in non-urban and mountainous environments due to the scarcity of unobstructed and geometrically well-defined surfaces. This is compounded by the presence of unknown changes over time in potentially large parts of the scanned scenes. These challenges complicate the establishment of suitable correspondences between the scans. Traditional registration methods often struggle under these conditions, leading to point cloud differences that may be misinterpreted and mask the actual deformations. We apply an approach utilizing optical flow, as well as Feature to Feature Supervoxel-based Spatial Smoothing (F2S3), to determine 3D vector fields between corresponding points and robustly estimate the registration parameters from these correspondences. We conduct a comparative analysis of the registration accuracies achieved using the above methods and those obtained from traditional registration methods, including the Iterative Closest Point (ICP) algorithm. Target-based registration results serve as a benchmark for this analysis. Additionally, we study the impact of the various registration approaches on the estimated deformations and compare the TLS-based results to those obtained from plumb line measurements within the dam. The presented investigation uses real measurements from the Santa Maria dam in the Swiss Alps, but the findings are transferable to other geomonitoring application cases in non-urban environments.
  • An Approach for RGB-Guided Dense 3D Displacement Estimation in TLS-Based Geomonitoring
    Item type: Conference Paper
    Wang, Zhaoyi; Butt, Jemil Avers; Huang, Shengyu; et al. (2025)
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    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.
  • Sensitivity quantification of spatially averaged displacement estimates in TLS-based geomonitoring
    Item type: Journal Article
    Schmid, Lorenz; Medic, Tomislav; Meyer, Nicholas; et al. (2025)
    Journal of Applied Geodesy
    Terrestrial Laser Scanning (TLS) is increasingly used in geomonitoring for 3D displacement analysis. However, assessing the sensitivity of the implemented monitoring strategy, which is crucial for correctly interpreting observations and detecting deformations, is challenging due to the impact of complex spatial correlations and other influencing factors. Traditional methods for sensitivity analysis often assume uncorrelated measurements, leading to biased and overestimated sensitivity. This can lead to suboptimal choices of monitoring strategy, false expectations, and errors in displacement detection. This study introduces a new method for quantifying the uncertainty of spatially aggregated (averaged) TLS-based displacement estimates in geomonitoring by empirically locally sampling such aggregated values (ELSA). The method implicitly accounts for the mentioned spatial correlations and their local variations, therefore, providing a more realistic sensitivity quantification. Validation using real-world datasets and simulated displacements demonstrates the method’s ability to provide a realistic uncertainty estimate and, subsequently, a good sensitivity estimate, realized herein by means of the Minimal Detectable Bias (MDB). Finally, we investigated several data preprocessing steps and demonstrated their effectiveness in enhancing both sensitivity and the quality of uncertainty estimates.
Publications 1 - 4 of 4