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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2/W10-2025
https://doi.org/10.5194/isprs-archives-XLVIII-2-W10-2025-39-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-2-W10-2025-39-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
07 Jul 2025
| 07 Jul 2025
Analysis of refraction aware Neural Radiance Fields for 3D reconstruction of through the water scenes
Markus Brezovsky
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Anatol Günthner
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany
Frederik Schulte
Unit of Geometry and Surveying, Faculty of Engineering Sciences, University of Innsbruck, Innsbruck, Austria
Lukas Winiwarter
Unit of Geometry and Surveying, Faculty of Engineering Sciences, University of Innsbruck, Innsbruck, Austria
Boris Jutzi
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany
Gottfried Mandlburger
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
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Short summary
Short summary
We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
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M. Jäger, P. Hübner, D. Haitz, and B. Jutzi
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Earth Syst. Sci. Data, 14, 2989–3012, https://doi.org/10.5194/essd-14-2989-2022, https://doi.org/10.5194/essd-14-2989-2022, 2022
Short summary
Short summary
3D point clouds, acquired by laser scanning, allow us to retrieve information about forest structure and individual tree properties. We conducted airborne, UAV-borne and terrestrial laser scanning in German mixed forests, resulting in overlapping point clouds with different characteristics. From these, we generated a comprehensive database of individual tree point clouds and corresponding tree metrics. Our dataset may serve as a benchmark dataset for algorithms in forestry research.
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D. Haitz, B. Jutzi, P. Hübner, and M. Ulrich
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K. Anders, L. Winiwarter, D. Schröder, and B. Höfle
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