  We consider the problem of domain adaptation in LiDAR-based 3D object
detection. Towards this, we propose a simple yet effective training strategy
called Gradual Batch Alternation that can adapt from a large labeled source
domain to an insufficiently labeled target domain. The idea is to initiate the
training with the batch of samples from the source and target domain data in an
alternate fashion, but then gradually reduce the amount of the source domain
data over time as the training progresses. This way the model slowly shifts
towards the target domain and eventually better adapt to it. The domain
adaptation experiments for 3D object detection on four benchmark autonomous
driving datasets, namely ONCE, PandaSet, Waymo, and nuScenes, demonstrate
significant performance gains over prior arts and strong baselines.
