In autonomous driving, data augmentation is commonly used for improving 3D object detection. The most basic methods include insertion of copied objects and rotation and scaling of the entire training frame. Numerous variants have been developed as well. The existing methods, however, are considerably limited when compared to the variety of the real world possibilities. In this work, we develop a diversified and realistic augmentation method that can flexibly construct a whole-body object, freely locate and rotate the object, and apply self-occlusion and external-occlusion accordingly. To improve the diversity of the whole-body object construction, we develop an iterative method that stochastically combines multiple objects observed from the real world into a single object. Unlike the existing augmentation methods, the constructed objects can be randomly located and rotated in the training frame because proper occlusions can be reflected to the whole-body objects in the final step. Finally, proper self-occlusion at each local object level and external-occlusion at the global frame level are applied using the Hidden Point Removal (HPR) algorithm that is computationally efficient. HPR is also used for adaptively controlling the point density of each object according to the object's distance from the LiDAR. Experiment results show that the proposed DR.CPO algorithm is data-efficient and model-agnostic without incurring any computational overhead. Also, DR.CPO can improve mAP performance by 2.08% when compared to the best 3D detection result known for KITTI dataset. The code is available at https://github.com/SNU-DRL/DRCPO.git

翻译：在自动驾驶中，数据增强通常用于提升3D物体检测性能。最基本的方法包括复制物体的插入以及整个训练帧的旋转与缩放，此外也发展出了众多变体。然而，与真实世界中可能存在的多样性相比，现有方法存在显著局限。本文提出一种多样化且逼真的增强方法，能够灵活构建全身物体、自由定位与旋转物体，并相应应用自遮挡和外遮挡。为提升全身物体构建的多样性，我们开发了一种迭代方法，该方法随机组合从真实世界中观测到的多个物体以形成单一物体。与现有增强方法不同，所构建物体可在训练帧中随机定位和旋转，因为最终步骤中可通过合理遮挡反映到全身物体上。最后，利用计算高效的隐藏点移除（Hidden Point Removal, HPR）算法，在局部物体层面应用合适的自遮挡，并在全局帧层面应用外部遮挡。HPR还用于根据物体与激光雷达的距离自适应控制每个物体的点密度。实验结果表明，所提出的DR.CPO算法具有数据高效性和模型无关性，且不引入额外计算开销。此外，在KITTI数据集上，DR.CPO相较已知最佳3D检测结果可将mAP性能提升2.08%。代码开源地址：https://github.com/SNU-DRL/DRCPO.git