  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. Meanwhile, we model
the pixel-wise depth uncertainty to rectify the loss weights of noisy pseudo
labels. To avoid the large capacity gap induced by the strong Transformer
branch deteriorating the cross-distillation, we transfer the feature maps from
Transformer to CNN and design coupling units to assist the weak CNN branch to
leverage the transferred features. Furthermore, we propose a surprisingly
simple yet highly effective data augmentation technique CutFlip, which enforces
the model to exploit more valuable clues apart from the vertical image position
for depth inference. Extensive experiments demonstrate that our model,
termed~\textbf{URCDC-Depth}, exceeds previous state-of-the-art methods on the
KITTI, NYU-Depth-v2 and SUN RGB-D datasets, even with no additional
computational burden at inference time. The source code is publicly available
at \url{https://github.com/ShuweiShao/URCDC-Depth}.
