  Deep learning (DL) has become one of the mainstream and effective methods for
point cloud analysis tasks such as detection, segmentation and classification.
To reduce overfitting during training DL models and improve model performance
especially when the amount and/or diversity of training data are limited,
augmentation is often crucial. Although various point cloud data augmentation
methods have been widely used in different point cloud processing tasks, there
are currently no published systematic surveys or reviews of these methods.
Therefore, this article surveys these methods, categorizing them into a
taxonomy framework that comprises basic and specialized point cloud data
augmentation methods. Through a comprehensive evaluation of these augmentation
methods, this article identifies their potentials and limitations, serving as a
useful reference for choosing appropriate augmentation methods. In addition,
potential directions for future research are recommended. This survey
contributes to providing a holistic overview of the current state of point
cloud data augmentation, promoting its wider application and development.
