任何到完整：提示深度任何物以完成深度估计的一阶段
Any to Full: Prompting Depth Anything for Depth Completion in One Stage
- Rutgers University（罗格斯大学）
- Michigan State University（密歇根州立大学）
- JD Logistics（京东物流）
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Any2Full提出一种单阶段深度完成框架，通过尺度提示适应预训练MDE模型，提升鲁棒性和效率，优于现有方法。
AI中文摘要：
准确、密集的深度估计对于机器人感知至关重要，但商用传感器由于硬件限制往往会产生稀疏或不完整的测量。现有的RGBD融合深度完成方法学习先验条件，同时基于训练RGB分布和特定深度模式进行联合学习，限制了领域泛化能力和对各种深度模式的鲁棒性。最近的努力利用单目深度估计（MDE）模型引入领域通用的几何先验，但当前依赖显式相对-度量对齐的两阶段集成策略会引入额外的计算并产生结构扭曲。为此，我们提出了Any2Full，一种单阶段、领域通用且模式无关的框架，将完成重新公式化为对预训练MDE模型的尺度提示适应。为了解决深度稀疏程度不同和不规则的空间分布，我们设计了Scale-Aware Prompt Encoder。它从稀疏输入中蒸馏尺度提示，生成统一的尺度提示，引导MDE模型产生全局尺度一致的预测，同时保持其几何先验。大量实验表明，Any2Full在鲁棒性和效率方面均优于OMNI-DC，平均AbsREL提升32.2%，在相同MDE骨干下比PriorDA快1.4倍，建立了通用深度完成的新范式。代码和检查点可在https://github.com/zhiyuandaily/Any2Full获取。
英文摘要：
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 1.4$\times$ 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.
