  Current, self-supervised depth estimation architectures rely on clear and
sunny weather scenes to train deep neural networks. However, in many locations,
this assumption is too strong. For example in the UK (2021), 149 days consisted
of rain. For these architectures to be effective in real-world applications, we
must create models that can generalise to all weather conditions, times of the
day and image qualities. Using a combination of computer graphics and
generative models, one can augment existing sunny-weather data in a variety of
ways that simulate adverse weather effects. While it is tempting to use such
data augmentations for self-supervised depth, in the past this was shown to
degrade performance instead of improving it. In this paper, we put forward a
method that uses augmentations to remedy this problem. By exploiting the
correspondence between unaugmented and augmented data we introduce a
pseudo-supervised loss for both depth and pose estimation. This brings back
some of the benefits of supervised learning while still not requiring any
labels. We also make a series of practical recommendations which collectively
offer a reliable, efficient framework for weather-related augmentation of
self-supervised depth from monocular video. We present extensive testing to
show that our method, Robust-Depth, achieves SotA performance on the KITTI
dataset while significantly surpassing SotA on challenging, adverse condition
data such as DrivingStereo, Foggy CityScape and NuScenes-Night. The project
website can be found here https://kieran514.github.io/Robust-Depth-Project/.
