Wat3R: Underwater 3D Geometry Learning without Annotations Jiangwei Ren, Xingyu Jiang † , Zijie Song, Wei Xu, Hongkai Lin, Dingkang Liang and Xiang Bai Huazhong University of Science & Technology. (†) Corresponding author. Abstract 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 representations merely on abundant unlabeled real underwater video footage. We also design a cross view consistency loss that leverages geometric cues from other views to compensate for the information degradation in the current view caused by water attenuation and scattering. Furthermore, considering the lack of comprehensive evaluation benchmarks, we construct Water3D , a d…
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