  Data augmentation has been widely adopted for object detection in 3D point
clouds. However, all previous related efforts have focused on manually
designing specific data augmentation methods for individual architectures. In
this work, we present the first attempt to automate the design of data
augmentation policies for 3D object detection. We introduce the Progressive
Population Based Augmentation (PPBA) algorithm, which learns to optimize
augmentation strategies by narrowing down the search space and adopting the
best parameters discovered in previous iterations. On the KITTI 3D detection
test set, PPBA improves the StarNet detector by substantial margins on the
moderate difficulty category of cars, pedestrians, and cyclists, outperforming
all current state-of-the-art single-stage detection models. Additional
experiments on the Waymo Open Dataset indicate that PPBA continues to
effectively improve the StarNet and PointPillars detectors on a 20x larger
dataset compared to KITTI. The magnitude of the improvements may be comparable
to advances in 3D perception architectures and the gains come without an
incurred cost at inference time. In subsequent experiments, we find that PPBA
may be up to 10x more data efficient than baseline 3D detection models without
augmentation, highlighting that 3D detection models may achieve competitive
accuracy with far fewer labeled examples.
