  As 3D point cloud analysis has received increasing attention, the
insufficient scale of point cloud datasets and the weak generalization ability
of networks become prominent. In this paper, we propose a simple and effective
augmentation method for the point cloud data, named PointCutMix, to alleviate
those problems. It finds the optimal assignment between two point clouds and
generates new training data by replacing the points in one sample with their
optimal assigned pairs. Two replacement strategies are proposed to adapt to the
accuracy or robustness requirement for different tasks, one of which is to
randomly select all replacing points while the other one is to select k nearest
neighbors of a single random point. Both strategies consistently and
significantly improve the performance of various models on point cloud
classification problems. By introducing the saliency maps to guide the
selection of replacing points, the performance further improves. Moreover,
PointCutMix is validated to enhance the model robustness against the point
attack. It is worth noting that when using as a defense method, our method
outperforms the state-of-the-art defense algorithms. The code is available
at:https://github.com/cuge1995/PointCutMix
