We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models. 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 apply these corruptions to the ImageNet validation set to create 3D Common Corruptions (ImageNet-3DCC) benchmark. The evaluations on recent ImageNet models with robustness mechanisms show that ImageNet-3DCC is a challenging benchmark for object recognition task. Furthermore, it exposes vulnerabilities that are not captured by Common Corruptions, which can be informative during model development.