Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted
  Sample Selection
Shitao Chen Haolin Zhang Nanning Zheng
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
https://github.com/XJTU-Haolin/Point_Assisted_Sample_Selection.