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

Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training