StereoAdapter：將立體深度估計適應於水下場景

摘要

水下立體深度估計為機器人任務（如導航、檢測和地圖繪製）提供了精確的三維幾何信息，利用低成本被動相機獲取度量深度，同時避免了單目方法的尺度模糊性。然而，現有方法面臨兩個關鍵挑戰：（i）在缺乏大量標註數據的情況下，如何高效地將大型視覺基礎編碼器適應於水下領域；（ii）如何將全局一致但尺度模糊的單目先驗與局部度量但光度脆弱的立體對應緊密融合。為解決這些挑戰，我們提出了StereoAdapter，這是一個參數高效的自監督框架，集成了LoRA適應的單目基礎編碼器與遞歸立體精化模塊。我們進一步引入了動態LoRA適應，以實現高效的秩選擇，並在合成的UW-StereoDepth-40K數據集上進行預訓練，從而增強在各種水下條件下的魯棒性。在模擬和真實世界基準上的全面評估顯示，與最先進的方法相比，在TartanAir上提升了6.11%，在SQUID上提升了5.12%，而BlueROV2機器人的實際部署進一步證明了我們方法的一致魯棒性。代碼：https://github.com/AIGeeksGroup/StereoAdapter。網站：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.

StereoAdapter：將立體深度估計適應於水下場景

StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes

摘要

Support