---
pipeline_tag: depth-estimation
---

# StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes

This is the official repository for the paper:
[StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes](https://huggingface.co/papers/2509.16415) (arXiv: [2509.16415](https://arxiv.org/abs/2509.16415))

**Project Website**: https://aigeeksgroup.github.io/StereoAdapter/
**Code**: https://github.com/AIGeeksGroup/StereoAdapter
**Dataset**: https://huggingface.co/datasets/AIGeeksGroup/UW-StereoDepth-40K

<img src="./assets/stereoadapter_logo.png" alt="logo" width="50"/>

## Abstract
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.

## Citation
If you find our code or paper helpful, please consider starring ⭐ us and citing:

```bibtex
@article{wu2025stereoadapter,
  title={StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes},
  author={Wu, Zhengri and Wang, Yiran and Wen, Yu and Zhang, Zeyu and Wu, Biao and Tang, Hao},
  journal={arXiv preprint arXiv:2509.16415},
  year={2025}
}
```