StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes
Authors: name: Zhengri Wu, name: Yiran Wang, name: Yu Wen, name: Zeyu Zhang, name: Biao Wu, name: Hao Tang
Organization: Peking University

Underwater stereo depth estimation provides accurate 3D geometry for robotics
tasks such as navigation, inspection, and mapping, offering metric depth from
low-cost passive cameras while avoiding the scale ambiguity of monocular
methods. However, existing approaches face two critical challenges: (i)
parameter-efficiently adapting large vision foundation encoders to the
underwater domain without extensive labeled data, and (ii) tightly fusing
globally coherent but scale-ambiguous monocular priors with locally metric yet
photometrically fragile stereo correspondences. To address these challenges, we
propose StereoAdapter, a parameter-efficient self-supervised framework that
integrates a LoRA-adapted monocular foundation encoder with a recurrent stereo
refinement module. We further introduce dynamic LoRA adaptation for efficient
rank selection and pre-training on the synthetic UW-StereoDepth-40K dataset to
enhance robustness under diverse underwater conditions. Comprehensive
evaluations on both simulated and real-world benchmarks show improvements of
6.11% on TartanAir and 5.12% on SQUID compared to state-of-the-art methods,
while real-world deployment with the BlueROV2 robot further demonstrates the
consistent robustness of our approach. Code:
https://github.com/AIGeeksGroup/StereoAdapter. Website:
https://aigeeksgroup.github.io/StereoAdapter.