Swimm3R: Splatting with Medium-aware SfMfor Underwater 3D Reconstruction

Minseong Kweon and Junaed Sattar
University of Minnesota, IRV Lab
Under Review
arXiv Code (Coming Soon)

Interactive SfM Result

Compare underwater point clouds reconstructed by COLMAP, GLUEMAP, MASt3R-SfM, and Swimm3R across the four Barbados scenes. Drag to rotate, scroll to zoom.

Abstract

We propose Swimm3R, a unified framework that combines medium-aware structure-from-motion (SfM) with Underwater Beta Splatting to address scattering- and attenuation-induced failures in underwater 3D reconstruction. Swimm3R distills in-air geometric priors into a feed-forward backbone and uses a physics head to regress underwater image-formation parameters, camera poses, and restored point clouds. Additionally, we introduce Underwater Beta Splatting, which extends Gaussian splatting with Beta primitives and scattering-aware geometric gradients for stable underwater geometry representation. We further establish the Barbados underwater video dataset to demonstrate the effectiveness of our method in challenging underwater environments. On this dataset, Swimm3R robustly recovers underwater scene structure under challenging scattering conditions, yielding coherent seafloor geometry. Using these predicted point clouds, the proposed Underwater Beta Splatting improves average PSNR by 1.47 dB over WaterSplatting while increasing downstream localization performance by 2.0 and 2.4 percentage points in RRA@15 and RTA@15, respectively.

Swimm3R

Overview of the Swimm3R pipeline: LoRA fine-tuned medium-aware SfM feeding Underwater Beta Splatting
Overview of Swimm3R. Our model learns medium-aware SfM from raw underwater images via LoRA fine-tuning, whose estimated camera poses and point clouds provide rich geometric priors for underwater Beta splatting.

Performance Comparison

Qualitative comparison of RGB and depth across SfM initializations and renderers
Qualitative comparison of RGB and depth across SfM initializations (rows) and renderers (columns). UWBS preserves sharper scene structure and more consistent medium boundaries than prior methods, qualitatively suggesting more coherent appearance and depth under underwater scattering.
Quantitative comparison of test-frame rendering quality on the Barbados dataset
Quantitative comparison of test-frame rendering quality on the Barbados dataset across feed-forward SfM initializations and renderers.
Qualitative query-to-render feature matching for visual localization
Qualitative query-to-render feature matching for visual localization. UWBS produces more coherent matches across underwater appearance changes, showing that its rendered maps provide more reliable 2D correspondences and render-depth for downstream PnP pose estimation.

BibTeX

@article{kweon2026swimm3r,
  title   = {Swimm3R: Splatting with Medium-aware SfM for Underwater 3D Reconstruction},
  author  = {Kweon, Minseong and Sattar, Junaed},
  journal = {arXiv preprint arXiv:XXXX.XXXXX},
  year    = {2026}
}