Abstract
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.
Abstract (translated)
创建大量带有像素级标注的大LiDAR数据集是一项具有挑战性的任务。虽然已经开发了许多数据增强方法以减少对手动标注的依赖，但这些方法主要关注静态场景，并忽略了数据增强对于动态场景的重要性，这对于自动驾驶至关重要。为解决这个问题，我们提出了D-Aug，一种专为增强动态场景而设计的LiDAR数据增强方法。D-Aug从动态场景中提取物体，并将其插入其中，考虑这些物体在连续帧之间的连续性。为了实现无缝的插入到动态场景中，我们提出了一个基于动态碰撞检测和旋转对齐的参考引导方法。此外，我们还提出了一个用于确定插入位置的像素级道路识别策略，以提高数据增强的效率。我们使用各种3D检测和跟踪方法对 nuScenes 数据集进行了验证。比较实验证明了 D-Aug 的优越性。
URL
https://arxiv.org/abs/2404.11127
