  Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerable to
adversarial examples, threatening their practical deployment. Despite the many
research endeavors have been made to tackle this issue in recent years, the
diversity of adversarial examples on 3D point clouds makes them more
challenging to defend against than those on 2D images. For examples, attackers
can generate adversarial examples by adding, shifting, or removing points.
Consequently, existing defense strategies are hard to counter unseen point
cloud adversarial examples. In this paper, we first establish a comprehensive,
and rigorous point cloud adversarial robustness benchmark to evaluate
adversarial robustness, which can provide a detailed understanding of the
effects of the defense and attack methods. We then collect existing defense
tricks in point cloud adversarial defenses and then perform extensive and
systematic experiments to identify an effective combination of these tricks.
Furthermore, we propose a hybrid training augmentation methods that consider
various types of point cloud adversarial examples to adversarial training,
significantly improving the adversarial robustness. By combining these tricks,
we construct a more robust defense framework achieving an average accuracy of
83.45\% against various attacks, demonstrating its capability to enabling
robust learners. Our codebase are open-sourced on:
\url{https://github.com/qiufan319/benchmark_pc_attack.git}.
