3D object detection based on LiDAR point cloud and prior anchor boxes is a
critical technology for autonomous driving environment perception and
understanding. Nevertheless, an overlooked practical issue in existing methods
is the ambiguity in training sample allocation based on box Intersection over
Union (IoU_box). This problem impedes further enhancements in the performance
of anchor-based LiDAR 3D object detectors. To tackle this challenge, this paper
introduces a new training sample selection method that utilizes point cloud
distribution for anchor sample quality measurement, named Point Assisted Sample
Selection (PASS). This method has undergone rigorous evaluation on two widely
utilized datasets. Experimental results demonstrate that the application of
PASS elevates the average precision of anchor-based LiDAR 3D object detectors
to a novel state-of-the-art, thereby proving the effectiveness of the proposed
approach. The codes will be made available at
this https URL

基于 LiDAR 点云和先验锚点的 3D 物体检测是自动驾驶环境感知和理解的关键技术，然而现有方法中存在的一个被忽视的实际问题是基于盒子交并比（IoU_box）的训练样本分配中的歧义，这个问题阻碍了锚点为基础的 LiDAR 3D 物体检测器性能的进一步提升。为了解决这个挑战，本文引入了一种名为点辅助样本选择（PASS）的新的训练样本选择方法，该方法利用点云分布来衡量锚点样本的质量。该方法在两个广泛使用的数据集上经过了严格的评估，实验结果表明 PASS 的应用将锚点为基础的 LiDAR 3D 物体检测器的平均精度提升到了新的最新水平，从而证明了所提出方法的有效性。代码将在该 URL 中提供。