Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications

Understand

In recent years, self-supervised methods for monocular depth estimation has rapidly become an significant branch of depth estimation task, especially for autonomous driving applications.

• Despite the high overall precision achieved, current methods still suffer from a) imprecise object-level depth inference and b) uncertain scale factor.
• The former problem would cause texture copy or provide inaccurate object boundary, and the latter would require current methods to have an additional sensor like LiDAR to provide depth ground-truth or stereo camera as additional training inputs, which makes them difficult to implement.
• In this work, we propose to address these two problems together by introducing DNet.