StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes
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.
68d229631ca7156988a8eddb
Zhengri Wu
admin_assigned
2025-09-23T14:34:17.892Z
672dd7722408a6ad524ffd71
/avatars/5f2808c9ac960399ee8189674fcd906a.svg
Zhengri Wu
user
DefaultAaron
68d229631ca7156988a8eddc
Yiran Wang
68d229631ca7156988a8eddd
Yu Wen
68d229631ca7156988a8edde
Zeyu Zhang
admin_assigned
2025-09-23T14:34:10.259Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
68d229631ca7156988a8eddf
Biao Wu
68d229631ca7156988a8ede0
Hao Tang
StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes
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.
StereoAdapter is a parameter-efficient self-supervised framework that integrates a LoRA-adapted monocular encoder with a recurrent stereo refinement module for underwater stereo depth estimation, improving accuracy and robustness.
stereo depth estimation parameter-efficient self-supervised framework LoRA-adapted monocular foundation encoder recurrent stereo refinement module dynamic LoRA adaptation UW-StereoDepth-40K dataset TartanAir SQUID BlueROV2