Point cloud has a wide range of applications in areas such as autonomous driving, mapping, navigation, scene reconstruction, and medical imaging. Due to its great potentials in these applications, point cloud processing has gained great attention in the field of computer vision. Among various point cloud processing techniques, deep learning (DL) has become one of the mainstream and effective methods for 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 and discusses these methods and categorizes them into a taxonomy framework. Through the comprehensive evaluation and comparison of the augmentation methods, this article identifies their potentials and limitations and suggests possible future research directions. This work helps researchers gain a holistic understanding of the current status of point cloud data augmentation and promotes its wider application and development.

翻译：点云在自动驾驶、测绘、导航、场景重建及医学影像等领域具有广泛的应用前景。由于其在这些领域中的巨大潜力，点云处理已成为计算机视觉领域的研究热点。在众多点云处理技术中，深度学习已成为检测、分割和分类等任务的主流且有效方法之一。为减少深度学习模型训练过程中的过拟合现象，并在训练数据量或多样性有限时提升模型性能，数据增强技术往往至关重要。尽管各类点云数据增强方法已在不同点云处理任务中得到广泛应用，但目前尚未有公开发表的系统性综述或评述对这些方法进行梳理。因此，本文对现有方法进行了系统综述与探讨，并将其归纳为分类框架。通过对各类增强方法的全面评估与比较，本文揭示了其优势与局限性，并提出了潜在的未来研究方向。本工作有助于研究者全面把握点云数据增强的研究现状，推动其更广泛的应用与发展。