Digging Into Self-Supervised Monocular Depth Estimation

Understand

Per-pixel ground-truth depth data is challenging to acquire at scale.

• To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation.
• In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods.
• Research on self-supervised monocular training usually explores increasingly complex architectures, loss functions, and image formation models, all of which have recently helped to close the gap with fully-supervised methods.