  The prevalent approaches of unsupervised 3D object detection follow
cluster-based pseudo-label generation and iterative self-training processes.
However, the challenge arises due to the sparsity of LiDAR scans, which leads
to pseudo-labels with erroneous size and position, resulting in subpar
detection performance. To tackle this problem, this paper introduces a
Commonsense Prototype-based Detector, termed CPD, for unsupervised 3D object
detection. CPD first constructs Commonsense Prototype (CProto) characterized by
high-quality bounding box and dense points, based on commonsense intuition.
Subsequently, CPD refines the low-quality pseudo-labels by leveraging the size
prior from CProto. Furthermore, CPD enhances the detection accuracy of sparsely
scanned objects by the geometric knowledge from CProto. CPD outperforms
state-of-the-art unsupervised 3D detectors on Waymo Open Dataset (WOD),
PandaSet, and KITTI datasets by a large margin. Besides, by training CPD on WOD
and testing on KITTI, CPD attains 90.85% and 81.01% 3D Average Precision on
easy and moderate car classes, respectively. These achievements position CPD in
close proximity to fully supervised detectors, highlighting the significance of
our method. The code will be available at https://github.com/hailanyi/CPD.
