  To alleviate the high annotation cost in LiDAR-based 3D object detection,
active learning is a promising solution that learns to select only a small
portion of unlabeled data to annotate, without compromising model performance.
Our empirical study, however, suggests that mainstream uncertainty-based and
diversity-based active learning policies are not effective when applied in the
3D detection task, as they fail to balance the trade-off between point cloud
informativeness and box-level annotation costs. To overcome this limitation, we
jointly investigate three novel criteria in our framework Crb for point cloud
acquisition - label conciseness}, feature representativeness and geometric
balance, which hierarchically filters out the point clouds of redundant 3D
bounding box labels, latent features and geometric characteristics (e.g., point
cloud density) from the unlabeled sample pool and greedily selects informative
ones with fewer objects to annotate. Our theoretical analysis demonstrates that
the proposed criteria align the marginal distributions of the selected subset
and the prior distributions of the unseen test set, and minimizes the upper
bound of the generalization error. To validate the effectiveness and
applicability of Crb, we conduct extensive experiments on the two benchmark 3D
object detection datasets of KITTI and Waymo and examine both one-stage (i.e.,
Second) and two-stage 3D detectors (i.e., Pv-rcnn). Experiments evidence that
the proposed approach outperforms existing active learning strategies and
achieves fully supervised performance requiring $1\%$ and $8\%$ annotations of
bounding boxes and point clouds, respectively. Source code:
https://github.com/Luoyadan/CRB-active-3Ddet.
