Creating large LiDAR datasets with pixel-level labeling poses significant
challenges. While numerous data augmentation methods have been developed to
reduce the reliance on manual labeling, these methods predominantly focus on
static scenes and they overlook the importance of data augmentation for dynamic
scenes, which is critical for autonomous driving. To address this issue, we
propose D-Aug, a LiDAR data augmentation method tailored for augmenting dynamic
scenes. D-Aug extracts objects and inserts them into dynamic scenes,
considering the continuity of these objects across consecutive frames. For
seamless insertion into dynamic scenes, we propose a reference-guided method
that involves dynamic collision detection and rotation alignment. Additionally,
we present a pixel-level road identification strategy to efficiently determine
suitable insertion positions. We validated our method using the nuScenes
dataset with various 3D detection and tracking methods. Comparative experiments
demonstrate the superiority of D-Aug.

提出了一种适用于动态场景的 LiDAR 数据增强方法 D-Aug，该方法通过提取对象并将其插入动态场景，考虑这些对象在连续帧之间的连续性，并采用参考引导方法实现无缝插入动态场景，同时还提出了像素级道路识别策略来有效确定适合的插入位置，通过与不同的 3D 检测和跟踪方法在 nuScenes 数据集上进行验证，实验证明了 D-Aug 方法的优越性。