URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation

Understand

This work aims to estimate a high-quality depth map from a single RGB image.

• Due to the lack of depth clues, making full use of the long-range correlation and the local information is critical for accurate depth estimation.
• Towards this end, we introduce an uncertainty rectified cross-distillation between Transformer and convolutional neural network (CNN) to learn a unified depth estimator.
• Specifically, we use the depth estimates from the Transformer branch and the CNN branch as pseudo labels to teach each other.