  Self-supervised monocular depth estimation is a salient task for 3D scene
understanding. Learned jointly with monocular ego-motion estimation, several
methods have been proposed to predict accurate pixel-wise depth without using
labeled data. Nevertheless, these methods focus on improving performance under
ideal conditions without natural or digital corruptions. The general absence of
occlusions is assumed even for object-specific depth estimation. These methods
are also vulnerable to adversarial attacks, which is a pertinent concern for
their reliable deployment in robots and autonomous driving systems. We propose
MIMDepth, a method that adapts masked image modeling (MIM) for self-supervised
monocular depth estimation. While MIM has been used to learn generalizable
features during pre-training, we show how it could be adapted for direct
training of monocular depth estimation. Our experiments show that MIMDepth is
more robust to noise, blur, weather conditions, digital artifacts, occlusions,
as well as untargeted and targeted adversarial attacks.
