Tomislav Medic

Last Name

Medic

First Name

Tomislav

ORCID

0000-0001-6332-5783

Organisational unit

03964 - Wieser, Andreas / Wieser, Andreas

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Publications 1 - 10 of 26

Assessing the alignment between geometry and colors in TLS colored point
Wang, Zhaoyi; Varga, Matej; Medic, Tomislav; et al. (2023)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The integration of the color information from RGB cameras with the point cloud geometry is used in numerous applications. However, little attention has been paid on errors that occur when aligning colors to points in terrestrial laser scanning (TLS) point clouds. Such errors may impact the performance of algorithms that utilize colored point clouds. Herein, we propose a procedure for assessing the alignment between the TLS point cloud geometry and colors. The procedure is based upon identifying artificial targets observed in both LiDAR-based point cloud intensity data and camera-based RGB data, and quantifying the quality of the alignment using differences between the target center coordinates estimated separately from these two data sources. Experimental results with eight scanners show that the quality of the alignment depends on the scanner, the software used for colorizing the point clouds, and may change with changing environmental conditions. While we found the effects of misalignment to be negligible for some scanners, they exhibited clearly systematic patterns exceeding the beam divergence, image and scan resolution for four of the scanners. The maximum deviations were about 2 mrad perpendicular to the line-of-sight when colorizing the point clouds with the respective manufacturer’s software or scanner in-built functions, while they were up to about 5 mrad when using a different software. Testing the alignment quality, e.g., using the approach presented herein, is thus important for applications requiring accurate alignment of the RGB colors with the point cloud geometry.
Vibration monitoring of a bridge using 2D profile laser scanning: Lessons learned from the comparison of two spatio-temporal processing strategies
Meyer, Nicholas; Schmid, Lorenz; Wieser, Andreas; et al. (2023)
Proceedings of the 5th Joint International Symposium on Deformation Monitoring - JISDM 2022
Profile laser scanning allows sub-millimeter precise contact-free measurements with high spatial and temporal resolution. That makes it an appealing solution for structural health monitoring focusing on vibrations of engineering structures, such as the analysis of eigenmodes and eigenfrequencies of bridges. In this work, we use the profile scanning mode of a Zoller+Fröhlich Imager 5016 terrestrial laser scanner (TLS) to observe bridge dynamics, focusing on the free decay processes following trains passing the bridge and exciting the structure. We compare two vibration monitoring strategies and implement an open-source semi-automatic software that integrates both approaches. We successfully estimate a spatio-temporal vibration model (including dampening coefficient) despite the maximum vibration amplitude reaching only 0.3 mm during the free decay process. Both strategies allow the estimation of the first eigenfrequency with a precision better than 0.1 Hz. Within the paper, we highlight the advantages and tackle the identified challenges of these vibration monitoring strategies. We also report on a preliminary investigation of appropriate instrument positioning for estimating the parameters of a spatio-temporal vibration model.
Georeferencing of terrestrial radar images in geomonitoring using kernel correlation
Schmid, Lorenz; Medic, Tomislav; Collins, Brian D.; et al. (2023)
International Journal of Remote Sensing
Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument’s pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over 1km . This is an improvement of almost one order of magnitude compared to what had been achieved using standard approaches and direct observation of the radar instrument’s pose. The proposed method thus contributes to the automation of TRI data processing and improved localization of small-scale deformation areas detected in radar images.
Finding the Best TLS Point Cloud Registration Algorithm for Long-Range Geomonitoring
Laasch, Helena; Jacquemart, Mylène; Ruttner, Pia; et al. (2023)
Ingenieurvermessung 23: Beiträge zum 20. Internationalen Ingenieurvermessungskurs Zürich
Accurate registration of TLS (Terrestrial Laser Scanner) point clouds is essential for unbiased determination of deformations in long-range geomonitoring. We evaluated the performance of several established registration methods in an ongoing geomonitoring case study. Our results showed that the ICP (Iterative Closest Point)-based methods specifically developed with geomonitoring in mind perform the best. However, when dealing with distortions in point clouds, the newly developed stripe-wise non-rigid transformation based on the F2S3 (feature to feature supervoxel-based spatial smoothing) algorithm outperformed all established methods.
Towards Assessing Sandstone Surface Moisture and Degradation Level from Radiometrically Corrected TLS Intensity Data
Laasch, Helena; Medic, Tomislav; Wieser, Andreas (2023)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Water is a prevalent deterioration agent for historic masonry works, especially those made of clay-bearing sandstones. To preserve cultural heritage made of sandstone, it is important to monitor, and then detect the regions with water retention or stone deterioration. To that aim, we investigate the prospects of terrestrial laser scanner (TLS) intensities for quantifying moisture in sandstone. Through a series of experiments following the drying processes of sandstone samples, we verify that TLS intensities can serve as moisture proxies for remote-sensing water retention. We identify the theoretically most suitable wavelengths, systematic effects requiring mitigation, and promising mitigation strategies. However, we also observe that the intensities are significantly affected by the type of sandstone and its level of degradation. Our results indicate that it is possible to distinguish different sandstones and levels of artificial degradation by observing and analyzing TLS-intensity time series during the drying process.
Remotely sensing inner fruit quality using multispectral LiDAR: Estimating sugar and dry matter content in apples
Medic, Tomislav; Ray, Pabitro; Han, Yu; et al. (2024)
Computers and Electronics in Agriculture
Diffuse reflectance spectroscopy is a well-established non-destructive technique for in-situ estimation of internal fruit quality properties. However, the operating range of the conventionally used instruments is limited to a few cm and often requires direct surface contact with the fruit. Alternative non-destructive approaches, such as hyperspectral imaging, allow for space between the sensor and the object, but in return, they require controlled illumination conditions commonly realized using dark chambers. In this work, we present a novel approach toward remote sensing of relevant fruit quality parameters on the case study of estimating total soluble solids (TSS) and dry matter content (DMC) in apples using a prototype supercontinuum-based multispectral LiDAR (MSL). Experimental results are acquired over a stand-off range of 0.5 m under uncontrolled illumination conditions. The spectral data is acquired across the 580–900 nm spectral range of the supercontinuum source, and R2 of 0.73 is achieved for estimating TSS and DMC using a random forest regression. These results on the estimated parameters are comparable to those reported previously in the literature for in-house developed prototypes relying on fruit contact or immediate proximity. In contrast, our experiments demonstrated TSS and DMC estimation at larger distances relative to typical reflectance spectroscopy instruments and without controlled illumination conditions typically mandated by hyperspectral imaging. Moreover, we demonstrate how our results translate to the estimation of TSS and DMC from experimentally generated multispectral 3D point clouds at a stand-off range of 5 m, demonstrating the potential of simultaneous acquisition of spectral and geometrical data at even higher ranges, showcasing the possibility of new use-cases.
Estimating Dry Matter and Total Soluble Content in Apples Using a Commercial Portable Hyperspectral Imaging System
Medic, Tomislav (2023)
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The quest for rapid, non-destructive, and precise technologies for fruit quality estimation is motivated by the needs across the whole food production chain. One of the emerging technologies fulfilling these requirements is spectral imaging. However, despite documented successes, the technology is yet to become established in commercial applications. The best results reported in the literature rely on fixed, non-portable dedicated setups, and controlled light conditions, which limits the potential use cases along the food production chain. In our study, we investigate the possibility of estimating dry matter content (DMC) and total soluble content (TSC) of store-bought apples in non-regulated indoor conditions using a commercial, portable, hand-held imaging system featuring a hyperspectral camera. The acquired images are transformed into per-fruit representative spectral profiles, pre-processed, and analyzed using partial least squares (PLS), the established method in the chemometrics community. We achieved the R2 of 0.93 for TSC and 0.91 for DMC on the test dataset, with a mean absolute error of 0.71 °Brix for TSC and 0.7% for DMC, which is comparable to the state-of-the-art results presented in the literature. These results indicate that recent instrumental developments enable the deployment of spectral imaging systems in a wider range of tasks in food production, requiring portability and allowing for less stringent control of environmental conditions.
Dense 3D displacement estimation for landslide monitoring via fusion of TLS point clouds and embedded RGB images
Wang, Zhaoyi; Butt, Jemil Avers; Huang, Shengyu; et al. (2026)
International Journal of Applied Earth Observation and Geoinformation
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 fuses 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.
Quantifying and Reducing the Uncertainty of 3D Displacement Estimates from Terrestrial Laser Scanner Point Clouds
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
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.

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Tomislav Medic

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