ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs

Understand

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.