Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey
It addresses the problem of limited data and overfitting in point cloud analysis for researchers and practitioners, but it is incremental as it synthesizes existing work without introducing new methods.
This survey tackles the lack of systematic reviews on point cloud data augmentation methods for deep learning, categorizing them into a taxonomy and evaluating their potentials and limitations to serve as a reference for choosing appropriate methods.
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.