Wat3R: Underwater 3D Geometry Learning without Annotations
Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations. Pioneering methods rely on massive dense annotations that are impractical in underwater settings. In this paper, we propose Wat3R, a cross-domain semi-supervised learning framework designed to adapt feed-forward 3D reconstruction models from air to underwater scenes. Uniquely, our method eliminates the need for any annotated underwater data following a teacher-student architecture, that learns robust geometry representat
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 49%isl-org/Open3D →
- LinkedLinked via arxiv author · 85%Jiangwei Ren →
“Wat3R: Underwater 3D Geometry Learning without Annotations”
- LinkedLinked via arxiv author · 85%Xingyu Jiang →
“Wat3R: Underwater 3D Geometry Learning without Annotations”
- LinkedLinked via arxiv author · 85%Zijie Song →
“Wat3R: Underwater 3D Geometry Learning without Annotations”
- LinkedLinked via arxiv author · 85%Ziwei Xu →
“Wat3R: Underwater 3D Geometry Learning without Annotations”
- LinkedLinked via arxiv author · 85%Hongkai Lin →
“Wat3R: Underwater 3D Geometry Learning without Annotations”
- LinkedLinked via arxiv author · 85%Dingkang Liang →
“Wat3R: Underwater 3D Geometry Learning without Annotations”
- LinkedLinked via arxiv author · 85%Xiang Bai →
“Wat3R: Underwater 3D Geometry Learning without Annotations”