  Existing self-supervised monocular depth estimation methods can get rid of
expensive annotations and achieve promising results. However, these methods
suffer from severe performance degradation when directly adopting a model
trained on a fixed resolution to evaluate at other different resolutions. In
this paper, we propose a resolution adaptive self-supervised monocular depth
estimation method (RA-Depth) by learning the scale invariance of the scene
depth. Specifically, we propose a simple yet efficient data augmentation method
to generate images with arbitrary scales for the same scene. Then, we develop a
dual high-resolution network that uses the multi-path encoder and decoder with
dense interactions to aggregate multi-scale features for accurate depth
inference. Finally, to explicitly learn the scale invariance of the scene
depth, we formulate a cross-scale depth consistency loss on depth predictions
with different scales. Extensive experiments on the KITTI, Make3D and NYU-V2
datasets demonstrate that RA-Depth not only achieves state-of-the-art
performance, but also exhibits a good ability of resolution adaptation.
