  3D reconstruction of depth and motion from monocular video in dynamic
environments is a highly ill-posed problem due to scale ambiguities when
projecting to the 2D image domain. In this work, we investigate the performance
of the current State-of-the-Art (SotA) deep multi-view systems in such
environments. We find that current supervised methods work surprisingly well
despite not modelling individual object motions, but make systematic errors due
to a lack of dense ground truth data. To detect such errors during usage, we
extend the cost volume based Deep Video to Depth (DeepV2D) framework
\cite{teed2018deepv2d} with a learned uncertainty. Our Deep Video to certain
Depth (DeepV2cD) model allows i) to perform en par or better with current SotA
and ii) achieve a better uncertainty measure than the naive Shannon entropy.
Our experiments show that a simple filter strategy based on the uncertainty can
significantly reduce systematic errors. This results in cleaner reconstructions
both on static and dynamic parts of the scene.
