StereoAdapter：将立体深度估计适配至水下场景

StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes

September 19, 2025

作者: Zhengri Wu, Yiran Wang, Yu Wen, Zeyu Zhang, Biao Wu, Hao Tang

cs.AI

摘要

水下立体深度估计为机器人导航、检测和地图构建等任务提供了精确的三维几何信息，通过低成本被动相机实现度量深度，同时避免了单目方法的尺度模糊问题。然而，现有方法面临两大关键挑战：(i) 在缺乏大量标注数据的情况下，如何参数高效地将大规模视觉基础编码器适配至水下领域；(ii) 如何将全局一致但尺度模糊的单目先验与局部度量但光度脆弱的立体对应关系紧密融合。为解决这些挑战，我们提出了StereoAdapter，一个参数高效的自监督框架，该框架集成了LoRA适配的单目基础编码器与递归立体优化模块。我们进一步引入了动态LoRA适配机制，用于高效秩选择，并在合成数据集UW-StereoDepth-40K上进行预训练，以增强方法在多样化水下环境下的鲁棒性。在模拟和真实世界基准上的全面评估显示，与最先进方法相比，StereoAdapter在TartanAir上提升了6.11%，在SQUID上提升了5.12%，而通过BlueROV2机器人的实际部署进一步验证了该方法的一致鲁棒性。代码与网站链接如下：代码仓库：https://github.com/AIGeeksGroup/StereoAdapter，项目主页：https://aigeeksgroup.github.io/StereoAdapter。

English

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.