Commonsense Prototype for Outdoor Unsupervised 3D Object Detection

The prevalent approaches of unsupervised 3D object de-tection 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 de-tection. CPD first constructs Commonsense Prototype (CProto) characterized by high-quality bounding box and dense points, based on commonsense intuition. Subse-quently, CPD refines the low-quality pseudo-labels by lever-aging the size prior from CProto. Furthermore, CPD en-hances the detection accuracy of sparsely scanned objects by the geometric knowledge from CProto. CPD outper-forms 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 Aver-age Precision on easy and moderate car classes, respectively. These achievements position CPD in close prox-imity to fully supervised detectors, highlighting the sig-nificance of our method. The code will be available at https://github.com/hailanyi/CPD.