  Self-supervised monocular depth estimation approaches suffer not only from
scale ambiguity but also infer temporally inconsistent depth maps w.r.t. scale.
While disambiguating scale during training is not possible without some kind of
ground truth supervision, having scale consistent depth predictions would make
it possible to calculate scale once during inference as a post-processing step
and use it over-time. With this as a goal, a set of temporal consistency losses
that minimize pose inconsistencies over time are introduced. Evaluations show
that introducing these constraints not only reduces depth inconsistencies but
also improves the baseline performance of depth and ego-motion prediction.
