  Autonomous driving datasets are often skewed and in particular, lack training
data for objects at farther distances from the ego vehicle. The imbalance of
data causes a performance degradation as the distance of the detected objects
increases. In this paper, we propose pattern-aware ground truth sampling, a
data augmentation technique that downsamples an object's point cloud based on
the LiDAR's characteristics. Specifically, we mimic the natural diverging point
pattern variation that occurs for objects at depth to simulate samples at
farther distances. Thus, the network has more diverse training examples and can
generalize to detecting farther objects more effectively. We evaluate against
existing data augmentation techniques that use point removal or perturbation
methods and find that our method outperforms all of them. Additionally, we
propose using equal element AP bins to evaluate the performance of 3D object
detectors across distance. We improve the performance of PV-RCNN on the car
class by more than 0.7 percent on the KITTI validation split at distances
greater than 25 m.
