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

arXiv: https://arxiv.org/abs/2605.10174 · HTML: https://mlsanity.com/paper/2605.10174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 6 of 6)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [SeaThru-NeRF: Neural Radiance Fields in Scattering Media](https://mlsanity.com/paper/2304.07743/) (2023)

## Abstract (arXiv)

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.