  The purpose of this work is to review the state-of-the-art LiDAR-based 3D
object detection methods, datasets, and challenges. We describe novel data
augmentation methods, sampling strategies, activation functions, attention
mechanisms, and regularization methods. Furthermore, we list recently
introduced normalization methods, learning rate schedules and loss functions.
Moreover, we also cover advantages and limitations of 10 novel autonomous
driving datasets. We evaluate novel 3D object detectors on the KITTI, nuScenes,
and Waymo dataset and show their accuracy, speed, and robustness. Finally, we
mention the current challenges in 3D object detection in LiDAR point clouds and
list some open issues.
