  Transferring the absolute depth prediction capabilities of an estimator to a
new domain is a task with significant real-world applications. This task is
specifically challenging when images from the new domain are collected without
ground-truth depth measurements, and possibly with sensors of different
intrinsics. To overcome such limitations, a recent zero-shot solution was
trained on an extensive training dataset and encoded the various camera
intrinsics. Other solutions generated synthetic data with depth labels that
matched the intrinsics of the new target data to enable depth-scale transfer
between the domains.
  In this work we present an alternative solution that can utilize any existing
synthetic or real dataset, that has a small number of images annotated with
ground truth depth labels. Specifically, we show that self-supervised depth
estimators result in up-to-scale predictions that are linearly correlated to
their absolute depth values across the domain, a property that we model in this
work using a single scalar. In addition, aligning the field-of-view of two
datasets prior to training, results in a common linear relationship for both
domains. We use this observed property to transfer the depth-scale from source
datasets that have absolute depth labels to new target datasets that lack these
measurements, enabling absolute depth predictions in the target domain.
  The suggested method was successfully demonstrated on the KITTI, DDAD and
nuScenes datasets, while using other existing real or synthetic source
datasets, that have a different field-of-view, other image style or structural
content, achieving comparable or better accuracy than other existing methods
that do not use target ground-truth depths.
