Enhancing self-supervised monocular depth estimation with traditional visual odometry

Understand

Estimating depth from a single image represents an attractive alternative to more traditional approaches leveraging multiple cameras.

• In this field, deep learning yielded outstanding results at the cost of needing large amounts of data labeled with precise depth measurements for training.
• An issue softened by self-supervised approaches leveraging monocular sequences or stereo pairs in place of expensive ground truth depth annotations.
• This paper enables to further improve monocular depth estimation by integrating into existing self-supervised networks a geometrical prior.