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

Accurate, dense depth estimation is crucial for robotic per-ception, but commodity sensors often yield sparse or incomplete measure-ments due to hardware limitations. Existing RGBD-fused depth comple-tion methods learn priors jointly conditioned on training RGB distri-bution and specific depth patterns, limiting domain generalization androbustness to various depth patterns. Recent efforts leverage monoculardepth estimation (MDE) models to introduce domain-general geomet-ric priors, but current two-stage integration strategies relying on explicitrelative-to-metric alignment incur additional computation and introducestructured distortions. To this end, we present Any2Full, a one-stage,domain-general, and pattern-agnostic framework that reformulates com-pletion as a scale-prompting adaptation of a pretrained MDE model.To address varying depth sparsity levels and irregular spatial distribu-tions, we design a Scale-Aware Prompt Encoder. It distills scale cuesfrom sparse inputs into unified scale prompts, guiding the MDE modeltoward globally scale-consistent predictions while preserving its geomet-ric priors. Extensive experiments demonstrate that Any2Full achievessuperior robustness and efficiency. It outperforms OMNI-DC by 32.2%in average AbsREL and delivers a 1.4× speedup over PriorDA with thesame MDE backbone, establishing a new paradigm for universal depthcompletion. Codes and checkpoints are available at https://github.com/zhiyuandaily/Any2Full.