Supervising radar depth completion using the monocular depth large model

In recent years, radar depth completion has made significant advances in developing backbone networks and high-quality datasets. However, less attention has been paid to optimizing the supervision manner. In this work, we propose a novel supervision method, to the best of our knowledge, using a relative-to-metric conversion (R2MC) module to leverage the generalization capability of the monocular depth large model (MDLM). The R2MC module employs sparse LiDAR data to obtain metric depth scales through pixelwise local mapping while preserving the generalization capability of the MDLM. The experimental results illustrate that our R2MC module can be combined with different backbones and improve their performance compared to their original supervision manners.