  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.
