  In this paper we address the benefit of adding adversarial training to the
task of monocular depth estimation. A model can be trained in a self-supervised
setting on stereo pairs of images, where depth (disparities) are an
intermediate result in a right-to-left image reconstruction pipeline. For the
quality of the image reconstruction and disparity prediction, a combination of
different losses is used, including L1 image reconstruction losses and
left-right disparity smoothness. These are local pixel-wise losses, while depth
prediction requires global consistency. Therefore, we extend the
self-supervised network to become a Generative Adversarial Network (GAN), by
including a discriminator which should tell apart reconstructed (fake) images
from real images. We evaluate Vanilla GANs, LSGANs and Wasserstein GANs in
combination with different pixel-wise reconstruction losses. Based on extensive
experimental evaluation, we conclude that adversarial training is beneficial if
and only if the reconstruction loss is not too constrained. Even though
adversarial training seems promising because it promotes global consistency,
non-adversarial training outperforms (or is on par with) any method trained
with a GAN when a constrained reconstruction loss is used in combination with
batch normalisation. Based on the insights of our experimental evaluation we
obtain state-of-the art monocular depth estimation results by using batch
normalisation and different output scales.
