POINTACL: Adversarial Contrastive Learning for Robust Point Clouds Representation Under Adversarial Attack

Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models. In contrastive learning, a projector which consists of multilayer perceptron (MLP) will project high dimension 3D point cloud feature into low dimension for calculating contrastive loss during contrastive pretraining.We propose a novel method for generating high-quality 3D adversarial examples for adversarial training, which leverages the virtual adversarial loss with the feature representations prior to projection in a contrastive learning framework. To train the self-supervised contrastive learning framework adversarially, we introduce our robust aware loss function. Additionally, we show that incorporating high difference points using the Difference of Normal (DoN) operator as an additional input for adversarial self-supervised contrastive learning can significantly enhance the adversarial robustness of the pre-trained model. Our proposed method, POINTACL, is evaluated on several downstream tasks, including 3D classification and 3D segmentation using multiple datasets. Our experimental results demonstrate that POINTACL achieves state-of-the-art performance in terms of robust accuracy when compared to other contrastive adversarial learning methods.