  Dense depth estimation is essential to scene-understanding for autonomous
driving. However, recent self-supervised approaches on monocular videos suffer
from scale-inconsistency across long sequences. Utilizing data from the
ubiquitously copresent global positioning systems (GPS), we tackle this
challenge by proposing a dynamically-weighted GPS-to-Scale (g2s) loss to
complement the appearance-based losses. We emphasize that the GPS is needed
only during the multimodal training, and not at inference. The relative
distance between frames captured through the GPS provides a scale signal that
is independent of the camera setup and scene distribution, resulting in richer
learned feature representations. Through extensive evaluation on multiple
datasets, we demonstrate scale-consistent and -aware depth estimation during
inference, improving the performance even when training with low-frequency GPS
data.
