基于稀疏先验的单目深度估计算法，实现实时水下三维感知。
SPADE: Sparsity Adaptive Depth Estimator for Zero-Shot, Real-Time, Monocular Depth Estimation in Underwater Environments
- 融合预训练相对深度与稀疏深度点，生成稠密度量深度图。
- 在嵌入式设备上达到15帧以上/秒，精度优于现有方法。
- 适用于复杂结构和浑浊水域的无人潜航器自主巡检。
水下基础设施因海洋环境严苛需频繁检测与维护。当前依赖人工潜水员或遥控潜水器，受限于感知与操作挑战，尤其在复杂结构或浑浊水域中表现不佳。提升水下车辆的空间感知能力是降低操控风险、实现更高自主性的关键。为此，我们提出SPADE：Sparsity Adaptive Depth Estimator，一种结合预训练相对深度估计器与稀疏深度先验的单目深度估计流程，生成稠密且具有度量尺度的深度图。该两阶段方法首先用稀疏深度点对相对深度图进行尺度校准，再通过提出的级联卷积-可变形注意力模块优化最终度量预测。所提方法在多个基准上优于现有最先进模型，并可在嵌入式硬件上以超过15 FPS的速度运行，有望支持实际水下检测与干预任务。本工作已提交至IEEE Journal of Oceanic Engineering AUV 2026特刊。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。