BathyFacto: Refraction-Aware Two-Media Neural Radiance Fields for Bathymetry

Through-water photogrammetry from UAV imagery enables shallow-water bathymetry, but refraction at the air--water interface violates the straight-ray assumption of Structure-from-Motion and causes systematic depth bias. We present BathyFacto, a refraction-aware two-media extension of Nerfacto in Nerfstudio for metrically consistent underwater point clouds on simulated data. BathyFacto uses a shared hash-grid density field with a medium-conditioned color head and traces each camera ray as two segments: a straight air segment to a planar water surface and a refracted water segment computed using Snell's law and known refractive indices. A single proposal-network sampler operates on a virtual straight ray, while a kinked density wrapper corrects water-segment positions before density evaluation. Our pipeline converts photogrammetric reconstructions to Nerfstudio format, estimates the water plane from boundary markers, provides per-pixel medium masks, and supports refraction-corrected point-cloud export with reversible transforms to world and global frames. On a simulated scene with ground truth, BathyFacto achieves a Cloud-to-Mesh signed median deviation of $-0.001$,m and 85.7,% completeness at 0.2,m tolerance in the absolute global frame without rigid-body alignment. This compares with $+1.370$,m / 11.6,% for Nerfacto and $+1.409$,m / 9.9,% for BathyFacto without refraction. Even after a naive refractive-index depth correction, both baselines remain offset by approximately 0.4,m. Unlike a refraction-corrected Multi-View Stereo reference, which is reliable mainly for near-nadir views, BathyFacto recovers consistent geometry across the full range of camera incidence angles.