  Notwithstanding the prominent performance achieved in various applications,
point cloud recognition models have often suffered from natural corruptions and
adversarial perturbations. In this paper, we delve into boosting the general
robustness of point cloud recognition models and propose Point-Cloud
Contrastive Adversarial Training (PointCAT). The main intuition of PointCAT is
encouraging the target recognition model to narrow the decision gap between
clean point clouds and corrupted point clouds. Specifically, we leverage a
supervised contrastive loss to facilitate the alignment and uniformity of the
hypersphere features extracted by the recognition model, and design a pair of
centralizing losses with the dynamic prototype guidance to avoid these features
deviating from their belonging category clusters. To provide the more
challenging corrupted point clouds, we adversarially train a noise generator
along with the recognition model from the scratch, instead of using
gradient-based attack as the inner loop like previous adversarial training
methods. Comprehensive experiments show that the proposed PointCAT outperforms
the baseline methods and dramatically boosts the robustness of different point
cloud recognition models, under a variety of corruptions including isotropic
point noises, the LiDAR simulated noises, random point dropping and adversarial
perturbations.
