  Photometric consistency loss is one of the representative objective functions
commonly used for self-supervised monocular depth estimation. However, this
loss often causes unstable depth predictions in textureless or occluded regions
due to incorrect guidance. Recent self-supervised learning approaches tackle
this issue by utilizing feature representations explicitly learned from
auto-encoders, expecting better discriminability than the input image. Despite
the use of auto-encoded features, we observe that the method does not embed
features as discriminative as auto-encoded features. In this paper, we propose
residual guidance loss that enables the depth estimation network to embed the
discriminative feature by transferring the discriminability of auto-encoded
features. We conducted experiments on the KITTI benchmark and verified our
method's superiority and orthogonality on other state-of-the-art methods.
