Aqua3R: Feed-Forward Underwater Reconstruction with Dual Gaussian Scenes

Water attenuation and backscatter couple scene appearance with the surrounding medium, so underwater reconstruction requires both reproducing the observed images and recovering the underlying water-free scene. Per-scene methods can address both goals, but require optimization from calibrated cameras, while existing feed-forward approaches do not explicitly separate in-water and restored scene representations. We present Aqua3R, a pose-free feed-forward model that predicts cameras, in-water and restored Gaussian scenes, and an explicit medium model in a single forward pass. The two scene representations share camera estimates and pre-fusion depth points and diverge only at the attribute heads. The medium model then re-renders the restored scene to reproduce the underwater observation. We further introduce DL3DV-Water, which provides paired clean and underwater views with formation parameters, and DeepSea-ROV, which contains raw deep-sea videos with registered clips. We also construct transfer sets and an evaluation protocol covering all predicted outputs. At 24 views, Aqua3R improves in-water PSNR by 0.98–2.75 dB over the stronger of two underwater-adapted YoNoSplat baselines on real footage and two synthetic transfer sets, while producing substantially more accurate camera estimates on real footage. In a controlled data study, broader medium coverage improves physical re-rendering PSNR on 25 real clips by an average of 1.06 dB. In contrast, gains from increasing the number of source scenes depend on the training design, and additional conditions or optimization updates do not yield consistent improvements. We will release the data and code, along with model checkpoints and per-scene results.