  Deep approaches to predict monocular depth and ego-motion have grown in
recent years due to their ability to produce dense depth from monocular images.
The main idea behind them is to optimize the photometric consistency over image
sequences by warping one view into another, similar to direct visual odometry
methods. One major drawback is that these methods infer depth from a single
view, which might not effectively capture the relation between pixels.
Moreover, simply minimizing the photometric loss does not ensure proper pixel
correspondences, which is a key factor for accurate depth and pose estimations.
  In contrast, we propose a 2-view depth network to infer the scene depth from
consecutive frames, thereby learning inter-pixel relationships. To ensure
better correspondences, thereby better geometric understanding, we propose
incorporating epipolar constraints to make the learning more geometrically
sound. We use the Essential matrix obtained using Nist'er's Five Point
Algorithm, to enforce meaningful geometric constraints, rather than using it as
training labels. This allows us to use lesser no. of trainable parameters
compared to state-of-the-art methods. The proposed method results in better
depth images and pose estimates, which capture the scene structure and motion
in a better way. Such a geometrically constrained learning performs
successfully even in cases where simply minimizing the photometric error would
fail.
