  Monocular depth estimation is an extensively studied computer vision problem
with a vast variety of applications. Deep learning-based methods have
demonstrated promise for both supervised and unsupervised depth estimation from
monocular images. Most existing approaches treat depth estimation as a
regression problem with a local pixel-wise loss function. In this work, we
innovate beyond existing approaches by using adversarial training to learn a
context-aware, non-local loss function. Such an approach penalizes the joint
configuration of predicted depth values at the patch-level instead of the
pixel-level, which allows networks to incorporate more global information. In
this framework, the generator learns a mapping between RGB images and its
corresponding depth map, while the discriminator learns to distinguish depth
map and RGB pairs from ground truth. This conditional GAN depth estimation
framework is stabilized using spectral normalization to prevent mode collapse
when learning from diverse datasets. We test this approach using a diverse set
of generators that include U-Net and joint CNN-CRF. We benchmark this approach
on the NYUv2, Make3D and KITTI datasets, and observe that adversarial training
reduces relative error by several fold, achieving state-of-the-art performance.
