  Self-supervised methods have been proven effective for learning deep
representations of 3D point cloud data. Although recent methods in this domain
often rely on random masking of inputs, the results of this approach can be
improved. We introduce PointCAM, a novel adversarial method for learning a
masking function for point clouds. Our model utilizes a self-distillation
framework with an online tokenizer for 3D point clouds. Compared to previous
techniques that optimize patch-level and object-level objectives, we postulate
applying an auxiliary network that learns how to select masks instead of
choosing them randomly. Our results show that the learned masking function
achieves state-of-the-art or competitive performance on various downstream
tasks. The source code is available at https://github.com/szacho/pointcam.
