LiDAR data of urban scenarios poses unique challenges, such as heterogeneous characteristics and inherent class imbalance. Therefore, large-scale datasets are necessary to apply deep learning methods. Instance augmentation has emerged as an efficient method to increase dataset diversity. However, current methods require the time-consuming curation of 3D models or costly manual data annotation. To overcome these limitations, we propose Text3DAug, a novel approach leveraging generative models for instance augmentation. Text3DAug does not depend on labeled data and is the first of its kind to generate instances and annotations from text. This allows for a fully automated pipeline, eliminating the need for manual effort in practical applications. Additionally, Text3DAug is sensor agnostic and can be applied regardless of the LiDAR sensor used. Comprehensive experimental analysis on LiDAR segmentation, detection and novel class discovery demonstrates that Text3DAug is effective in supplementing existing methods or as a standalone method, performing on par or better than established methods, however while overcoming their specific drawbacks. The code is publicly available.

本研究针对城市场景中激光雷达数据的异质特性和类不平衡问题，提出了一种新的实例增强方法——Text3DAug。该方法利用生成模型从文本生成实例和注释，完全自动化，避免了手动数据注释的需求，且可适用于不同类型的激光雷达传感器。实验结果表明，Text3DAug在激光雷达分割、检测和新类发现等任务上性能卓越，能够有效补充现有方法或作为独立方法使用。

Text3DAug -- 利用提示实例增强的激光雷达感知