Self-Supervised Learning of Depth and Camera Motion from 360{\deg} Videos

Understand

As 360{\deg} cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360{\deg} perception becomes more and more important.

• We propose a novel self-supervised learning approach for predicting the omnidirectional depth and camera motion from a 360{\deg} video.
• In particular, starting from the SfMLearner, which is designed for cameras with normal field-of-view, we introduce three key features to process 360{\deg} images efficiently.
• Firstly, we convert each image from equirectangular projection to cubic projection in order to avoid image distortion.