  This paper aims to address the problem of supervised monocular depth
estimation. We start with a meticulous pilot study to demonstrate that the
long-range correlation is essential for accurate depth estimation. Therefore,
we propose to leverage the Transformer to model this global context with an
effective attention mechanism. We also adopt an additional convolution branch
to preserve the local information as the Transformer lacks the spatial
inductive bias in modeling such contents. However, independent branches lead to
a shortage of connections between features. To bridge this gap, we design a
hierarchical aggregation and heterogeneous interaction module to enhance the
Transformer features via element-wise interaction and model the affinity
between the Transformer and the CNN features in a set-to-set translation
manner. Due to the unbearable memory cost caused by global attention on
high-resolution feature maps, we introduce the deformable scheme to reduce the
complexity. Extensive experiments on the KITTI, NYU, and SUN RGB-D datasets
demonstrate that our proposed model, termed DepthFormer, surpasses
state-of-the-art monocular depth estimation methods with prominent margins.
Notably, it achieves the most competitive result on the highly competitive
KITTI depth estimation benchmark. Our codes and models are available at
https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox.
