3D Common Corruptions and Data Augmentation

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 show these transformations are `efficient' (can be computed on-the-fly), `extendable' (can be applied on most datasets of real images), expose vulnerability of existing models, and can effectively make models more robust when employed as `3D data augmentation' mechanisms. Our evaluations performed on several tasks and datasets suggest incorporating 3D information into robustness benchmarking and training opens up a promising direction for robustness research.