  Self-supervised monocular depth estimation that does not require ground truth
for training has attracted attention in recent years. It is of high interest to
design lightweight but effective models so that they can be deployed on edge
devices. Many existing architectures benefit from using heavier backbones at
the expense of model sizes. This paper achieves comparable results with a
lightweight architecture. Specifically, the efficient combination of CNNs and
Transformers is investigated, and a hybrid architecture called Lite-Mono is
presented. A Consecutive Dilated Convolutions (CDC) module and a Local-Global
Features Interaction (LGFI) module are proposed. The former is used to extract
rich multi-scale local features, and the latter takes advantage of the
self-attention mechanism to encode long-range global information into the
features. Experiments demonstrate that Lite-Mono outperforms Monodepth2 by a
large margin in accuracy, with about 80% fewer trainable parameters.
