  An autonomous driving system requires a 3D object detector, which must
perceive all present road agents reliably to navigate an environment safely.
However, real-world driving datasets often suffer from the problem of data
imbalance, which causes difficulties in training a model that works well across
all classes, resulting in an undesired imbalanced sub-optimal performance. In
this work, we propose a method to address this data imbalance problem. Our
method consists of two main components: (i) a LiDAR-based 3D object detector
with per-class multiple detection heads where losses from each head are
modified by dynamic weight average to be balanced. (ii) Contextual ground truth
(GT) sampling, where we improve conventional GT sampling techniques by
leveraging semantic information to augment point cloud with sampled ground
truth GT objects. Our experiment with KITTI and nuScenes datasets confirms our
proposed method's effectiveness in dealing with the data imbalance problem,
producing better detection accuracy compared to existing approaches.
