Preprints
https://doi.org/10.5194/egusphere-2025-1110
https://doi.org/10.5194/egusphere-2025-1110
31 Mar 2025
 | 31 Mar 2025

Curvature-based pebble segmentation for reconstructed surface meshes

Aljoscha Rheinwalt, Benjamin Purinton, and Bodo Bookhagen

Abstract. Accurate segmentation of pebbles in complex 3D scenes is essential to understand sediment transport and river dynamics. In this study, we present a curvature-based instance segmentation approach for detecting and characterizing pebbles from 3D surface reconstructions. Our method is validated using high-resolution ground truth models, allowing for a quantitative assessment of segmentation accuracy. The workflow involves reconstructing a sandbox scene using available open-source or commercial software packages, segmenting individual pebbles based on curvature features, and evaluating segmentation performance using detection metrics, primary axes estimation, 3D orientation retrieval, and surface area comparisons. Results show a high detection precision (0.980), with segmentation errors primarily attributed to under-segmentation caused by overly smooth surface reconstructions. Primary axis estimation via bounding box fitting proves more reliable than ellipsoid fitting, particularly for the A and B axes, while the C-axis remains the most challenging due to partial occlusion. 3D orientation estimation reveals variability, with cumulative errors ranging from less than 5° to more than 45°, highlighting the difficulty in retrieving full orientations from incomplete segments. Surface area metrics indicate that our approach prioritizes precision over recall, with nine out of ten ground truth pebbles achieving IoU values above 0.8. In addition, we introduce a Python-based segmentation tool that provides detailed morphological and color-based metrics for each detected pebble. Our findings emphasize the advantages of true 3D analysis over traditional 2D photo-sieving approaches and suggest future improvements through refined segmentation algorithms and enhanced surface reconstructions.

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Journal article(s) based on this preprint

24 Sep 2025
Curvature-based pebble segmentation for reconstructed surface meshes
Aljoscha Rheinwalt, Benjamin Purinton, and Bodo Bookhagen
Earth Surf. Dynam., 13, 923–940, https://doi.org/10.5194/esurf-13-923-2025,https://doi.org/10.5194/esurf-13-923-2025, 2025
Short summary
Aljoscha Rheinwalt, Benjamin Purinton, and Bodo Bookhagen

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1110', Anonymous Referee #1, 28 Apr 2025
  • RC2: 'Comment on egusphere-2025-1110', Anonymous Referee #2, 01 May 2025

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1110', Anonymous Referee #1, 28 Apr 2025
  • RC2: 'Comment on egusphere-2025-1110', Anonymous Referee #2, 01 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Aljoscha Rheinwalt on behalf of the Authors (08 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (25 Jun 2025) by Anne Baar
ED: Publish as is (17 Jul 2025) by Wolfgang Schwanghart (Editor)
AR by Aljoscha Rheinwalt on behalf of the Authors (17 Jul 2025)

Journal article(s) based on this preprint

24 Sep 2025
Curvature-based pebble segmentation for reconstructed surface meshes
Aljoscha Rheinwalt, Benjamin Purinton, and Bodo Bookhagen
Earth Surf. Dynam., 13, 923–940, https://doi.org/10.5194/esurf-13-923-2025,https://doi.org/10.5194/esurf-13-923-2025, 2025
Short summary
Aljoscha Rheinwalt, Benjamin Purinton, and Bodo Bookhagen

Data sets

Curvature-based pebble segmentation for reconstructed surface meshes Aljoscha Rheinwalt, Benjamin Purinton, and Bodo Bookhagen https://doi.org/10.5281/zenodo.14987824

Aljoscha Rheinwalt, Benjamin Purinton, and Bodo Bookhagen

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Short summary
Our study presents a computer-based method to detect and measure pebbles in 3D models reconstructed from camera photos. We tested it in a controlled setup and achieved 98 % accuracy in detecting pebbles. Unlike traditional 2D methods, our approach provides full 3D size and orientation data. This improves sediment analysis and riverbed studies by offering more precise measurements. Our work highlights the potential of 3D modeling for studying natural surfaces.
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