{"ID":2869022,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16415","arxiv_id":"2509.16415","title":"StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes","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. Code: https://github.com/AIGeeksGroup/StereoAdapter. Website: https://aigeeksgroup.github.io/StereoAdapter.","short_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-efficient...","url_abs":"https://arxiv.org/abs/2509.16415","url_pdf":"https://arxiv.org/pdf/2509.16415v1","authors":"[\"Zhengri Wu\",\"Yiran Wang\",\"Yu Wen\",\"Zeyu Zhang\",\"Biao Wu\",\"Hao Tang\"]","published":"2025-09-19T20:57:03Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.RO\"]","methods":"[\"LoRA\"]","has_code":false,"code_links":[{"ID":609644,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2869022,"paper_url":"https://arxiv.org/abs/2509.16415","paper_title":"StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes","repo_url":"https://github.com/AIGeeksGroup/StereoAdapter","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}