Title: Benchmarking and Improving Robustness of 3D Point Cloud Recognition against Common Corruptions. Abstract: 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, a 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 models. 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. To bridge this gap, we further propose RobustNet and PointCutMixup that embrace the merits of existing architectural designs to further improve the corruption robustness in the 3D point cloud domain, after evaluating a wide range of augmentation and test-time adaptation strategies. Our codebase and dataset are open-sourced.