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 derived 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 depth labels. To avoid the large performance 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 utilize 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 clue of vertical image position for depth estimation. Extensive experiments indicate that our model, termed~\textbf{URCDC-Depth}, exceeds previous state-of-the-art methods on the KITTI and NYU-Depth-v2 datasets, even with no additional computational burden at inference time. The source code is publicly available at \url{https://github.com/ShuweiShao/URCDC-Depth}.

翻译：本研究旨在从单张RGB图像中估计高质量深度图。由于缺乏深度线索，充分利用长程关联性与局部信息对于精确深度估计至关重要。为此，我们提出一种基于Transformer与卷积神经网络（CNN）间不确定性校正的交叉蒸馏方法，以学习统一的深度估计器。具体而言，我们将Transformer分支与CNN分支生成的深度估计值作为伪标签进行相互教学，同时建模像素级深度不确定性以校正噪声深度标签的损失权重。为避免强Transformer分支导致的性能差距破坏交叉蒸馏，我们将特征图从Transformer迁移至CNN，并设计耦合单元辅助弱CNN分支利用迁移特征。此外，我们提出一种简洁而高效的数据增强技术CutFlip，迫使模型在垂直图像位置线索之外挖掘更有价值的深度估计线索。大量实验表明，所提模型URCDC-Depth在KITTI与NYU-Depth-v2数据集上超越现有最佳方法，且推理阶段无额外计算负担。源代码已公开于\url{https://github.com/ShuweiShao/URCDC-Depth}。