On-Site Adaptation for Monocular Depth Estimation with a Static Camera

We introduce a novel technique for easing the deployment of an off-the-shelf monocular depth estimation network in unseen environments. Specifically, we target a very diffused setting with a fixed camera mounted higher over the ground to monitor an environment and highlight the limitations of state-of-the-art monocular networks deployed in such a setup. Purposely, we develop an on-site adaptation technique capable of 1) improving the accuracy of estimated depth maps in the presence of moving subjects, such as pedestrians, cars, and others; 2) refining the overall structure of the predicted depth map, to make it more consistent with the real 3D structure of the scene; 3) recovering absolute metric depth, usually lost by state-of-the-art solutions. Experiments on synthetic and real datasets confirm the effectiveness of our proposal.