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pipeline_tag: depth-estimation |
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--- |
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# StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes |
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This is the official repository for the paper: |
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[StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes](https://huggingface.co/papers/2509.16415) (arXiv: [2509.16415](https://arxiv.org/abs/2509.16415)) |
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**Project Website**: https://aigeeksgroup.github.io/StereoAdapter/ |
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**Code**: https://github.com/AIGeeksGroup/StereoAdapter |
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**Dataset**: https://huggingface.co/datasets/AIGeeksGroup/UW-StereoDepth-40K |
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<img src="./assets/stereoadapter_logo.png" alt="logo" width="50"/> |
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## Abstract |
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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. |
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## Citation |
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If you find our code or paper helpful, please consider starring ⭐ us and citing: |
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```bibtex |
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@article{wu2025stereoadapter, |
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title={StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes}, |
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author={Wu, Zhengri and Wang, Yiran and Wen, Yu and Zhang, Zeyu and Wu, Biao and Tang, Hao}, |
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journal={arXiv preprint arXiv:2509.16415}, |
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year={2025} |
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} |
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``` |
