LIFT: Local Metric Alignment and Foundation-guided Propagation for Few-shot Depth Completion

Xiang Li, Xiaojin Gong

Few-shot depth completion aims to recover dense metric depth from sparse measurements using only a small number of target-domain annotations, offering a favorable balance between annotation cost and model adaptation. We propose LIFT, a framework that harnesses both depth and feature priors from a frozen pretrained monocular depth estimation (MDE) foundation model for few-shot depth completion. Specifically, LIFT first estimates local scale and shift parameters on a coarse grid and interpolates them into smooth, dense parameter fields, providing a spatially adaptive metric depth initialization. It then progressively refines this initialization through multi-scale depth propagation guided by foundation features. Finally, a lightweight residual decoder combines multi-level foundation features with propagation correction to refine the metric depth. Extensive experiments on NYUv2 and KITTI-DC demonstrate that our proposed method achieves state-of-the-art few-shot performance. The source code will soon be released.