Underwater infrastructure requires frequent inspection and maintenance due to harsh marine conditions. Current reliance on human divers or remotely operated vehicles is limited by perceptual and operational challenges, especially around complex structures or in turbid water. Enhancing the spatial awareness of underwater vehicles is key to reducing piloting risks and enabling greater autonomy. To address these challenges, we present SPADE: SParsity Adaptive Depth Estimator, a monocular depth estimation pipeline that combines pre-trained relative depth estimator with sparse depth priors to produce dense, metric scale depth maps. Our two-stage approach first scales the relative depth map with the sparse depth points, then refines the final metric prediction with our proposed Cascade Conv-Deformable Transformer blocks. Our approach achieves improved accuracy and generalisation over state-of-the-art baselines and runs efficiently at over 15 FPS on embedded hardware, promising to support practical underwater inspection and intervention. This work has been submitted to IEEE Journal of Oceanic Engineering Special Issue of AUV 2026.

翻译：水下基础设施因恶劣的海洋环境而需要频繁的检查与维护。目前依赖人类潜水员或遥控潜水器的做法受到感知与操作挑战的限制，特别是在复杂结构周围或浑浊水域中。增强水下航行器的空间感知能力是降低操控风险、实现更高自主性的关键。为应对这些挑战，我们提出SPADE：稀疏自适应深度估计器，这是一种单目深度估计流程，将预训练的相对深度估计器与稀疏深度先验相结合，以生成稠密的度量尺度深度图。我们的两阶段方法首先利用稀疏深度点对相对深度图进行尺度缩放，随后通过我们提出的级联卷积-可变形Transformer模块对最终度量预测进行细化。该方法在精度和泛化能力上优于现有先进基线，并在嵌入式硬件上以超过15 FPS的效率运行，有望为实际水下检测与作业提供支持。本工作已提交至IEEE Journal of Oceanic Engineering的AUV 2026特刊。