  Depth estimation from a single image is an important task that can be applied
to various fields in computer vision, and has grown rapidly with the
development of convolutional neural networks. In this paper, we propose a novel
structure and training strategy for monocular depth estimation to further
improve the prediction accuracy of the network. We deploy a hierarchical
transformer encoder to capture and convey the global context, and design a
lightweight yet powerful decoder to generate an estimated depth map while
considering local connectivity. By constructing connected paths between
multi-scale local features and the global decoding stream with our proposed
selective feature fusion module, the network can integrate both representations
and recover fine details. In addition, the proposed decoder shows better
performance than the previously proposed decoders, with considerably less
computational complexity. Furthermore, we improve the depth-specific
augmentation method by utilizing an important observation in depth estimation
to enhance the model. Our network achieves state-of-the-art performance over
the challenging depth dataset NYU Depth V2. Extensive experiments have been
conducted to validate and show the effectiveness of the proposed approach.
Finally, our model shows better generalisation ability and robustness than
other comparative models.
