Investigating the Potential of SfM, MVS, and Monocular Depth Estimation for Water Surface Reconstruction

Abstract. Reconstructing the water surface in refractive domains such as rivers and lakes is challenging, since light bending at the air-water interface alters the apparent geometry and breaks the straight-ray assumption of conventional image-based 3D reconstruction. Accurate water surface models are therefore a key prerequisite for many refraction-aware applications. This contribution investigates the potential of three passive image-based methods, Structure from Motion (SfM), Multi-View Stereo (MVS), and Monocular Depth Estimation (MDE), to derive a geometrically consistent water surface model from UAV imagery of the Pielach River study site in Austria. The dataset represents a demanding scenario with clear, fast-flowing water and low texture, which causes strong refraction and poor feature stability. Quantitative comparisons against LiDAR-derived reference surfaces show that SfM yields sparse and inconsistent points, MVS reconstructs the riverbed instead of the water surface, and MDE exhibits scale and offset inconsistencies despite explicit calibration using SfM reprojections. Completeness remains below 45% for all methods with mean vertical deviations in the decimetre-to-metre range. The results indicate that current image-based approaches are insufficient for reliable water-surface reconstruction in such settings, reinforcing the need for an explicitly derived surface model as input to refraction-aware modeling, for example in bathymetric reconstruction and future refractive neural rendering methods, rather than relying on implicitly learned water surfaces.