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 advanced point cloud data augmentation methods, according to their levels of complexity. 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.

翻译：深度学习(Deep Learning, DL)已成为点云分析任务（如检测、分割与分类）的主流有效方法之一。为减少训练深度学习模型时的过拟合现象，并在训练数据数量或多样性有限时提升模型性能，数据增强往往至关重要。尽管多种点云数据增强方法已广泛应用于不同点云处理任务，但目前尚未有公开发表的相关系统性综述或回顾。为此，本文对这些方法进行综述，并根据其复杂度将基本与高级点云数据增强方法纳入分类框架。通过对这些增强方法的全面评估，本文识别了其潜力与局限性，为选择合适增强方法提供有益参考。此外，本文还推荐了未来研究的潜在方向。本综述旨在全面概述点云数据增强的当前发展现状，促进其更广泛的应用与进步。