  Recovering structure and motion parameters given a image pair or a sequence
of images is a well studied problem in computer vision. This is often achieved
by employing Structure from Motion (SfM) or Simultaneous Localization and
Mapping (SLAM) algorithms based on the real-time requirements. Recently, with
the advent of Convolutional Neural Networks (CNNs) researchers have explored
the possibility of using machine learning techniques to reconstruct the 3D
structure of a scene and jointly predict the camera pose. In this work, we
present a framework that achieves state-of-the-art performance on single image
depth prediction for both indoor and outdoor scenes. The depth prediction
system is then extended to predict optical flow and ultimately the camera pose
and trained end-to-end. Our motion estimation framework outperforms the
previous motion prediction systems and we also demonstrate that the
state-of-the-art metric depths can be further improved using the knowledge of
pose.
