360$^\circ$ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer

Understand

To predict high-resolution (HR) omnidirectional depth map, existing methods typically leverage HR omnidirectional image (ODI) as the input via fully-supervised learning.

• However, in practice, taking HR ODI as input is undesired due to resource-constrained devices.
• In addition, depth maps are often with lower resolution than color images.
• Therefore, in this paper, we explore for the first time to estimate the HR omnidirectional depth directly from a low-resolution (LR) ODI, when no HR depth GT map is available.