Axial-View-Oriented Contrastive Adversarial Training for Robust Point Cloud Recognition

Contrastive adversarial training emerges as an effective approach to enhancing model robustness in safety-critical applications, particularly point cloud recognition for autonomous driving and medical imaging. However, existing point cloud adversarial training methods mainly emphasize global contrastive learning while overlooking local geometric variations induced by adversarial perturbations. Motivated by the spatial and intensity variations of perturbations across axial views, we propose AVOC, a novel local-global adversarial training framework that utilizes axial-view-oriented contrastive learning. This framework leverages the smallest axial view for local contrastive learning, as it exhibits the highest perturbation differences, and utilizes the largest axial view for global contrastive learning, as it preserves global structural consistency. We conduct comprehensive experiments across four representative architectures, demonstrating significant robustness improvements on widely-adopted recognition benchmarks, including ModelNet40, ShapeNetPart, ModelNet40-C, and ScanObjectNN-C, and further validate its effectiveness on the large-scale KITTI benchmark for 3D object detection. Our results across diverse perturbation scenarios, encompassing white-box attacks, black-box attacks, and natural perturbations, demonstrate the consistent and significant model robustness enhancement of our proposed method.