  While convolutional neural networks have shown a tremendous impact on various
computer vision tasks, they generally demonstrate limitations in explicitly
modeling long-range dependencies due to the intrinsic locality of the
convolution operation. Transformers, initially designed for natural language
processing tasks, have emerged as alternative architectures with innate global
self-attention mechanisms to capture long-range dependencies. In this paper, we
propose TransDepth, an architecture which benefits from both convolutional
neural networks and transformers. To avoid the network to loose its ability to
capture local-level details due to the adoption of transformers, we propose a
novel decoder which employs on attention mechanisms based on gates. Notably,
this is the first paper which applies transformers into pixel-wise prediction
problems involving continuous labels (i.e., monocular depth prediction and
surface normal estimation). Extensive experiments demonstrate that the proposed
TransDepth achieves state-of-the-art performance on three challenging datasets.
The source code and trained models are available at
https://github.com/ygjwd12345/TransDepth.
