Wat3R: Underwater 3D Geometry Learning without Underwater Annotations

Abstract

Estimating 3D geometry in underwater environments presentsunique challenges due to light attenuation, scattering, and the absenceof large-scale, high-quality 3D annotations. Pioneering methods rely onmassive dense annotations that are impractical in underwater settings.In this paper, we propose Wat3R, a cross-domain semi-supervised learn-ing framework designed to adapt feed-forward 3D reconstruction modelsfrom air to underwater scenes. Uniquely, our method eliminates the needfor any annotated underwater data following a teacher-student architec-ture, that learns robust geometry representations merely on abundantunlabeled real underwater video footage. We also design a cross-viewconsistency loss that leverages geometric cues from other views to com-pensate for the information degradation in the current view caused bywater attenuation and scattering. Furthermore, considering the lack ofcomprehensive evaluation benchmarks, we construct Water3D, a di-verse dataset covering various water bodies and underwater scenarios, de-signed for geometric task evaluation. Experimental results demonstratethat Wat3R outperforms current state-of-the-art methods in underwatermulti-view depth estimation and point cloud reconstruction. The datasetand code are available at https://github.com/LSXI7/Wat3R .