  Unsupervised domain adaptation (DA) with the aid of pseudo labeling
techniques has emerged as a crucial approach for domain-adaptive 3D object
detection. While effective, existing DA methods suffer from a substantial drop
in performance when applied to a multi-class training setting, due to the
co-existence of low-quality pseudo labels and class imbalance issues. In this
paper, we address this challenge by proposing a novel ReDB framework tailored
for learning to detect all classes at once. Our approach produces Reliable,
Diverse, and class-Balanced pseudo 3D boxes to iteratively guide the
self-training on a distributionally different target domain. To alleviate
disruptions caused by the environmental discrepancy (e.g., beam numbers), the
proposed cross-domain examination (CDE) assesses the correctness of pseudo
labels by copy-pasting target instances into a source environment and measuring
the prediction consistency. To reduce computational overhead and mitigate the
object shift (e.g., scales and point densities), we design an overlapped boxes
counting (OBC) metric that allows to uniformly downsample pseudo-labeled
objects across different geometric characteristics. To confront the issue of
inter-class imbalance, we progressively augment the target point clouds with a
class-balanced set of pseudo-labeled target instances and source objects, which
boosts recognition accuracies on both frequently appearing and rare classes.
Experimental results on three benchmark datasets using both voxel-based (i.e.,
SECOND) and point-based 3D detectors (i.e., PointRCNN) demonstrate that our
proposed ReDB approach outperforms existing 3D domain adaptation methods by a
large margin, improving 23.15% mAP on the nuScenes $\rightarrow$ KITTI task.
The code is available at https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet.
