  Self-supervised monocular depth estimation networks are trained to predict
scene depth using nearby frames as a supervision signal during training.
However, for many applications, sequence information in the form of video
frames is also available at test time. The vast majority of monocular networks
do not make use of this extra signal, thus ignoring valuable information that
could be used to improve the predicted depth. Those that do, either use
computationally expensive test-time refinement techniques or off-the-shelf
recurrent networks, which only indirectly make use of the geometric information
that is inherently available.
  We propose ManyDepth, an adaptive approach to dense depth estimation that can
make use of sequence information at test time, when it is available. Taking
inspiration from multi-view stereo, we propose a deep end-to-end cost volume
based approach that is trained using self-supervision only. We present a novel
consistency loss that encourages the network to ignore the cost volume when it
is deemed unreliable, e.g. in the case of moving objects, and an augmentation
scheme to cope with static cameras. Our detailed experiments on both KITTI and
Cityscapes show that we outperform all published self-supervised baselines,
including those that use single or multiple frames at test time.
