Abstract
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.
Abstract (translated)
大多数无监督的三维物体检测方法遵循基于聚类的伪标签生成和迭代自训练过程。然而，由于激光雷达扫描的稀疏性，导致伪标签具有错误的大小和位置，从而导致检测性能不佳。为了解决这个问题，本文引入了一种以常识原型为基础的检测器，称为CPD，用于无监督三维物体检测。CPD首先基于常识直觉构建了高质量的边界框和密集点的高质量常识原型（CProto）。然后，CPD通过利用CProto的大小先验来优化低质量伪标签。此外，CPD通过CProto的几何知识提高了稀疏扫描对象检测的准确性。CPD在Waymo Open Dataset（WOD）、PandaSet和KITTI数据集上优于最先进的无监督三维检测器。此外，通过在WOD和KITTI上训练CPD并进行测试，CPD在容易和 moderate 车辆类别上获得了90.85%和81.01%的3D平均精度。这些成就使CPD与完全监督的检测器相接近，强调了我们的方法的重要性。代码将在该https URL上可用。
URL
https://arxiv.org/abs/2404.16493
