深度学习中点云数据增强的进展：一项综述
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey
AI总结：
本文综述了深度学习中点云数据增强方法，将其分类为基础与专用方法，评估其潜力与局限，并为未来研究提供方向，以促进该领域的广泛应用与发展。
摘要：
深度学习（DL）已成为点云分析任务（如检测、分割和分类）的主流且有效的方法之一。为了减少训练DL模型时的过拟合，并在训练数据量和/或多样性有限时提升模型性能，数据增强通常至关重要。尽管各种点云数据增强方法已广泛应用于不同的点云处理任务，但目前尚无已发表的针对这些方法的系统性综述或回顾。因此，本文对这些方法进行了调研，将它们归类为一个分类框架，该框架包含基础和专用的点云数据增强方法。通过对这些增强方法的全面评估，本文指出了它们的潜力与局限性，可作为选择合适增强方法的有用参考。此外，还推荐了未来研究的潜在方向。本综述有助于提供点云数据增强当前状态的整体概览，促进其更广泛的应用与发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
