  There has been a recent surge of interest in learning to perceive depth from
monocular videos in an unsupervised fashion. A key challenge in this field is
achieving robust and accurate depth estimation in challenging scenarios,
particularly in regions with weak textures or where dynamic objects are
present. This study makes three major contributions by delving deeply into
dense correspondence priors to provide existing frameworks with explicit
geometric constraints. The first novelty is a contextual-geometric depth
consistency loss, which employs depth maps triangulated from dense
correspondences based on estimated ego-motion to guide the learning of depth
perception from contextual information, since explicitly triangulated depth
maps capture accurate relative distances among pixels. The second novelty
arises from the observation that there exists an explicit, deducible
relationship between optical flow divergence and depth gradient. A differential
property correlation loss is, therefore, designed to refine depth estimation
with a specific emphasis on local variations. The third novelty is a
bidirectional stream co-adjustment strategy that enhances the interaction
between rigid and optical flows, encouraging the former towards more accurate
correspondence and making the latter more adaptable across various scenarios
under the static scene hypotheses. DCPI-Depth, a framework that incorporates
all these innovative components and couples two bidirectional and collaborative
streams, achieves state-of-the-art performance and generalizability across
multiple public datasets, outperforming all existing prior arts. Specifically,
it demonstrates accurate depth estimation in texture-less and dynamic regions,
and shows more reasonable smoothness.
