Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions Supplementary Materials Jiachen Sun

Figure A. DGCNN leverages EdgeConv as their basic operation to extract features. Please refer to our codebase for detailed parameters like batch normalization and activation functions. A.2 Self-Supervised Learning T ask We follow exactly the same setting as Poursaeed et al. [8] and Sauder et al. [9] for 3D rotation and We illustrate the FoldingNet architecture in Figure C. In this section, we first introduce the detailed formulations of the adopted attack methods. B.1 Attack Method We introduce the detailed formulation of attack methods used in our study.