  Monocular depth estimation is an ill-posed problem as the same 2D image can
be projected from infinite 3D scenes. Although the leading algorithms in this
field have reported significant improvement, they are essentially geared to the
particular compound of pictorial observations and camera parameters (i.e.,
intrinsics and extrinsics), strongly limiting their generalizability in
real-world scenarios. To cope with this challenge, this paper proposes a novel
ground embedding module to decouple camera parameters from pictorial cues, thus
promoting the generalization capability. Given camera parameters, the proposed
module generates the ground depth, which is stacked with the input image and
referenced in the final depth prediction. A ground attention is designed in the
module to optimally combine ground depth with residual depth. Our ground
embedding is highly flexible and lightweight, leading to a plug-in module that
is amenable to be integrated into various depth estimation networks.
Experiments reveal that our approach achieves the state-of-the-art results on
popular benchmarks, and more importantly, renders significant generalization
improvement on a wide range of cross-domain tests.
