Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes

Understand

We present a fully data-driven method to compute depth from diverse monocular video sequences that contain large amounts of non-rigid objects, e.g., people.

• In order to learn reconstruction cues for non-rigid scenes, we introduce a new dataset consisting of stereo videos scraped in-the-wild.
• This dataset has a wide variety of scene types, and features large amounts of nonrigid objects, especially people.
• From this, we compute disparity maps to be used as supervision to train our approach.