Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

Understand

Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.

• Existing dynamic-object-focused methods only partially solved the mismatch problem at the training loss level.
• In this paper, we accordingly propose a novel multi-frame monocular depth prediction method to solve these problems at both the prediction and supervision loss levels.
• Our method, called DynamicDepth, is a new framework trained via a self-supervised cycle consistent learning scheme.