Recent monocular foundation models excel at zero-shot depth estimation, yet their outputs are inherently relative rather than metric, limiting direct use in robotics and autonomous driving. We leverage the fact that relative depth preserves global layout and boundaries: by calibrating it with sparse range measurements, we transform it into a pseudo metric depth prior. Building on this prior, we design a refinement network that follows the prior where reliable and deviates where necessary, enabling accurate metric predictions from very few labeled samples. The resulting system is particularly effective when curated validation data are unavailable, sustaining stable scale and sharp edges across few-shot regimes. These findings suggest that coupling foundation priors with sparse anchors is a practical route to robust, deployment-ready depth completion under real-world label scarcity.

翻译：近期的单目基础模型在零样本深度估计方面表现出色，但其输出本质上是相对深度而非度量深度，这限制了其在机器人学和自动驾驶中的直接应用。我们利用相对深度保留全局布局和边界这一事实：通过稀疏测距数据对其进行标定，将其转化为一种伪度量深度先验。基于此先验，我们设计了一个优化网络，该网络在可靠区域遵循先验，在必要时进行修正，从而能够仅用极少标注样本实现精确的度量深度预测。所构建的系统在缺乏精校验证数据时尤为有效，能够在少样本场景下保持稳定的尺度与清晰的边缘。这些结果表明，将基础先验与稀疏锚点相结合，是在现实世界标注稀缺条件下实现鲁棒、可部署深度补全的实用途径。