  We introduce a set of image transformations that can be used as corruptions
to evaluate the robustness of models as well as data augmentation mechanisms
for training neural networks. The primary distinction of the proposed
transformations is that, unlike existing approaches such as Common Corruptions,
the geometry of the scene is incorporated in the transformations -- thus
leading to corruptions that are more likely to occur in the real world. We also
introduce a set of semantic corruptions (e.g. natural object occlusions). We
show these transformations are `efficient' (can be computed on-the-fly),
`extendable' (can be applied on most image datasets), expose vulnerability of
existing models, and can effectively make models more robust when employed as
`3D data augmentation' mechanisms. The evaluations on several tasks and
datasets suggest incorporating 3D information into benchmarking and training
opens up a promising direction for robustness research.
