Ground truth augmentation (GT-Aug) is a common method for LiDAR-based object detection, as it enhances object density by leveraging ground truth bounding boxes (GT bboxes). However, directly applying GT-Aug to 4D Radar tensor data overlooks important measurements outside the GT bboxes-such as sidelobes-leading to synthetic distributions that deviate from real-world 4D Radar data. To address this limitation, we propose 4D Radar Ground Truth Augmentation (4DR GT-Aug). Our approach first augments LiDAR data and then converts it to 4D Radar data via a LiDAR-to-4D Radar data synthesis (L2RDaS) module, which explicitly accounts for measurements both inside and outside GT bboxes. In doing so, it produces 4D Radar data distributions that more closely resemble real-world measurements, thereby improving object detection accuracy. Experiments on the K-Radar dataset show that the proposed method achieves improved performance compared to conventional GT-Aug in object detection for 4D Radar. The implementation code is available at https://github.com/kaist-avelab/K-Radar.

翻译：真值增强（GT-Aug）是激光雷达（LiDAR）目标检测中常用的方法，其通过利用真值边界框（GT bboxes）来提升目标密度。然而，直接将GT-Aug应用于4D雷达张量数据会忽略GT边界框外的重要测量值——例如旁瓣——从而导致合成数据分布偏离真实世界的4D雷达数据。为克服这一局限，我们提出4D雷达真值增强（4DR GT-Aug）。该方法首先对LiDAR数据进行增强，随后通过一个LiDAR至4D雷达数据合成（L2RDaS）模块将其转换为4D雷达数据；该模块明确考虑了GT边界框内外的测量值。通过这一过程，所生成的4D雷达数据分布能更贴近真实测量结果，从而提升目标检测精度。在K-Radar数据集上的实验表明，相较于传统GT-Aug方法，所提方法在4D雷达目标检测中取得了更优的性能。实现代码已发布于 https://github.com/kaist-avelab/K-Radar。