  A common dilemma in 3D object detection for autonomous driving is that
high-quality, dense point clouds are only available during training, but not
testing. We use knowledge distillation to bridge the gap between a model
trained on high-quality inputs at training time and another tested on
low-quality inputs at inference time. In particular, we design a two-stage
training pipeline for point cloud object detection. First, we train an object
detection model on dense point clouds, which are generated from multiple frames
using extra information only available at training time. Then, we train the
model's identical counterpart on sparse single-frame point clouds with
consistency regularization on features from both models. We show that this
procedure improves performance on low-quality data during testing, without
additional overhead.
