AdversariallyRobust3DPointCloudRecognition UsingSelf-Supervisions SupplementaryMaterials

In this section, we introduce our implementation details of the adopted model architectures and self-supervisedlearningtasks. The EdgeConv layers are stacked to form the DGCNN backbone. As introduced in 2.2,we choose k = 3,4 in this task. We follow the attack setups in [13] to formulate our attack. Weprovide insights onhow different components contribute to the overall improvements.