Curvature-based pebble segmentation for reconstructed surface meshes

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 full 3D 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 9 out of 10 test pebbles achieving intersection-over-union 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 potential of 3D analysis over traditional 2D approaches and suggest future improvements through refined segmentation algorithms and enhanced surface reconstructions.