  Significant strides have been made in closed world 3D object detection,
testing systems in environments with known classes. However, the challenge
arises in open world scenarios where new object classes appear. Existing
efforts sequentially learn novel classes from streams of labeled data at a
significant annotation cost, impeding efficient deployment to the wild. To seek
effective solutions, we investigate a more practical yet challenging research
task: Open World Active Learning for 3D Object Detection (OWAL-3D), aiming at
selecting a small number of 3D boxes to annotate while maximizing detection
performance on both known and unknown classes. The core difficulty centers on
striking a balance between mining more unknown instances and minimizing the
labeling expenses of point clouds. Empirically, our study finds the harmonious
and inverse relationship between box quantities and their confidences can help
alleviate the dilemma, avoiding the repeated selection of common known
instances and focusing on uncertain objects that are potentially unknown. We
unify both relational constraints into a simple and effective AL strategy
namely OpenCRB, which guides to acquisition of informative point clouds with
the least amount of boxes to label. Furthermore, we develop a comprehensive
codebase for easy reproducing and future research, supporting 15 baseline
methods (i.e., active learning, out-of-distribution detection and open world
detection), 2 types of modern 3D detectors (i.e., one-stage SECOND and
two-stage PV-RCNN) and 3 benchmark 3D datasets (i.e., KITTI, nuScenes and
Waymo). Extensive experiments evidence that the proposed Open-CRB demonstrates
superiority and flexibility in recognizing both novel and shared categories
with very limited labeling costs, compared to state-of-the-art baselines.
