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 this https URL

本研究提出了一种基于常识原型的检测器（CPD），用于无监督的三维物体检测，通过构建高质量边界框和密集点的常识原型以及利用大小先验和几何知识改进了低质量伪标签，有效提高了检测性能。CPD 在多个数据集上取得了优于现有方法的结果，使其与全监督检测器非常接近，展示了该方法的重要性。