  In recent years, self-supervised methods for monocular depth estimation has
rapidly become an significant branch of depth estimation task, especially for
autonomous driving applications. Despite the high overall precision achieved,
current methods still suffer from a) imprecise object-level depth inference and
b) uncertain scale factor. The former problem would cause texture copy or
provide inaccurate object boundary, and the latter would require current
methods to have an additional sensor like LiDAR to provide depth ground-truth
or stereo camera as additional training inputs, which makes them difficult to
implement. In this work, we propose to address these two problems together by
introducing DNet. Our contributions are twofold: a) a novel dense connected
prediction (DCP) layer is proposed to provide better object-level depth
estimation and b) specifically for autonomous driving scenarios, dense
geometrical constrains (DGC) is introduced so that precise scale factor can be
recovered without additional cost for autonomous vehicles. Extensive
experiments have been conducted and, both DCP layer and DGC module are proved
to be effectively solving the aforementioned problems respectively. Thanks to
DCP layer, object boundary can now be better distinguished in the depth map and
the depth is more continues on object level. It is also demonstrated that the
performance of using DGC to perform scale recovery is comparable to that using
ground-truth information, when the camera height is given and the ground point
takes up more than 1.03\% of the pixels. Code is available at
https://github.com/TJ-IPLab/DNet.
