  Monocular depth estimators can be trained with various forms of
self-supervision from binocular-stereo data to circumvent the need for
high-quality laser scans or other ground-truth data. The disadvantage, however,
is that the photometric reprojection losses used with self-supervised learning
typically have multiple local minima. These plausible-looking alternatives to
ground truth can restrict what a regression network learns, causing it to
predict depth maps of limited quality. As one prominent example, depth
discontinuities around thin structures are often incorrectly estimated by
current state-of-the-art methods.
  Here, we study the problem of ambiguous reprojections in depth prediction
from stereo-based self-supervision, and introduce Depth Hints to alleviate
their effects. Depth Hints are complementary depth suggestions obtained from
simple off-the-shelf stereo algorithms. These hints enhance an existing
photometric loss function, and are used to guide a network to learn better
weights. They require no additional data, and are assumed to be right only
sometimes. We show that using our Depth Hints gives a substantial boost when
training several leading self-supervised-from-stereo models, not just our own.
Further, combined with other good practices, we produce state-of-the-art depth
predictions on the KITTI benchmark.
