← 研踪 PaperTrailarXiv:2611.10729 · v1
LIFT: Local Metric Alignment and Foundation-guided Propagation for Few-shot Depth Completion
首次提交 2026-11-07 21:42
版本更新 2026-11-07 21:42
雷达观测 2026-11-10 11:14
摘要
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.
作者备注：8 pages, 5 figures
观测到的版本
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v1 · 2026-11-07 21:42 · 最新观测版本
LIFT: Local Metric Alignment and Foundation-guided Propagation for Few-shot Depth Completion
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.
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