  This paper addresses the problem of Monocular Depth Estimation (MDE).
Existing approaches on MDE usually model it as a pixel-level regression
problem, ignoring the underlying geometry property. We empirically find this
may result in sub-optimal solution: while the predicted depth map presents
small loss value in one specific view, it may exhibit large loss if viewed in
different directions. In this paper, inspired by multi-view stereo (MVS), we
propose an Adversarial View-Consistent Learning (AVCL) framework to force the
estimated depth map to be all reasonable viewed from multiple views. To this
end, we first design a differentiable depth map warping operation, which is
end-to-end trainable, and then propose a pose generator to generate novel views
for a given image in an adversarial manner. Collaborating with the
differentiable depth map warping operation, the pose generator encourages the
depth estimation network to learn from hard views, hence produce
view-consistent depth maps . We evaluate our method on NYU Depth V2 dataset and
the experimental results show promising performance gain upon state-of-the-art
MDE approaches.
