  Recent advances in self-supervised learning havedemonstrated that it is
possible to learn accurate monoculardepth reconstruction from raw video data,
without using any 3Dground truth for supervision. However, in robotics
applications,multiple views of a scene may or may not be available, depend-ing
on the actions of the robot, switching between monocularand multi-view
reconstruction. To address this mixed setting,we proposed a new approach that
extends any off-the-shelfself-supervised monocular depth reconstruction system
to usemore than one image at test time. Our method builds on astandard prior
learned to perform monocular reconstruction,but uses self-supervision at test
time to further improve thereconstruction accuracy when multiple images are
available.When used to update the correct components of the model, thisapproach
is highly-effective. On the standard KITTI bench-mark, our self-supervised
method consistently outperformsall the previous methods with an average 25%
reduction inabsolute error for the three common setups (monocular, stereoand
monocular+stereo), and comes very close in accuracy whencompared to the
fully-supervised state-of-the-art methods.
