Learning Depth from Monocular Videos using Direct Methods

Understand

The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community.

• Unsupervised strategies to learning are particularly appealing as they can utilize much larger and varied monocular video datasets during learning without the need for ground truth depth or stereo.
• In previous works, separate pose and depth CNN predictors had to be determined such that their joint outputs minimized the photometric error.
• Inspired by recent advances in direct visual odometry (DVO), we argue that the depth CNN predictor can be learned without a pose CNN predictor.