AI RESEARCH
Any to Full: Prompting Depth Anything for Depth Completion in One Stage
arXiv CS.CV
•
ArXi:2603.05711v1 Announce Type: new 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 RGB distribution and specific depth patterns, limiting domain generalization and robustness to various depth patterns. Recent efforts leverage monocular depth estimation (MDE) models to