一步到位：基于深度泛化模型的单阶段深度补全提示法

Any to Full: Prompting Depth Anything for Depth Completion in One Stage

March 5, 2026

作者: Zhiyuan Zhou, Ruofeng Liu, Taichi Liu, Weijian Zuo, Shanshan Wang, Zhiqing Hong, Desheng Zhang

cs.AI

摘要

精确、稠密的深度估计对机器人感知至关重要，但商用传感器常因硬件限制产生稀疏或不完整的测量数据。现有RGBD融合深度补全方法需联合学习训练RGB分布与特定深度模式下的先验知识，这限制了其领域泛化能力及对多样化深度模式的鲁棒性。近期研究利用单目深度估计模型引入领域通用几何先验，但当前依赖显式相对-绝对尺度对齐的两阶段集成策略会带来额外计算量并引入结构化失真。为此，我们提出Any2Full——一种单阶段、领域通用且模式无关的框架，将深度补全重构为预训练单目深度估计模型的尺度提示自适应任务。针对深度稀疏程度不一和空间分布不规则的问题，我们设计了尺度感知提示编码器，从稀疏输入中提取尺度线索并转化为统一尺度提示，在保持模型几何先验的同时引导其生成全局尺度一致的预测。大量实验表明，Any2Full具备卓越的鲁棒性与效率：其平均AbsREL指标优于OMNI-DC达32.2%，在相同单目深度估计骨干网络下较PriorDA提速1.4倍，为通用深度补全建立了新范式。代码与模型已开源：https://github.com/zhiyuandaily/Any2Full。

English

Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-fused depth completion methods learn priors jointly conditioned on training RGB distribution and specific depth patterns, limiting domain generalization and robustness to various depth patterns. Recent efforts leverage monocular depth estimation (MDE) models to introduce domain-general geometric priors, but current two-stage integration strategies relying on explicit relative-to-metric alignment incur additional computation and introduce structured distortions. To this end, we present Any2Full, a one-stage, domain-general, and pattern-agnostic framework that reformulates completion as a scale-prompting adaptation of a pretrained MDE model. To address varying depth sparsity levels and irregular spatial distributions, we design a Scale-Aware Prompt Encoder. It distills scale cues from sparse inputs into unified scale prompts, guiding the MDE model toward globally scale-consistent predictions while preserving its geometric priors. Extensive experiments demonstrate that Any2Full achieves superior robustness and efficiency. It outperforms OMNI-DC by 32.2\% in average AbsREL and delivers a 1.4times speedup over PriorDA with the same MDE backbone, establishing a new paradigm for universal depth completion. Codes and checkpoints are available at https://github.com/zhiyuandaily/Any2Full.