  Deep neural networks on 3D point cloud data have been widely used in the real
world, especially in safety-critical applications. However, their robustness
against corruptions is less studied. In this paper, we present ModelNet40-C,
the first comprehensive benchmark on 3D point cloud corruption robustness,
consisting of 15 common and realistic corruptions. Our evaluation shows a
significant gap between the performances on ModelNet40 and ModelNet40-C for
state-of-the-art (SOTA) models. To reduce the gap, we propose a simple but
effective method by combining PointCutMix-R and TENT after evaluating a wide
range of augmentation and test-time adaptation strategies. We identify a number
of critical insights for future studies on corruption robustness in point cloud
recognition. For instance, we unveil that Transformer-based architectures with
proper training recipes achieve the strongest robustness. We hope our in-depth
analysis will motivate the development of robust training strategies or
architecture designs in the 3D point cloud domain. Our codebase and dataset are
included in https://github.com/jiachens/ModelNet40-C
