Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection
This addresses a practical issue in autonomous driving perception, though it appears incremental as it refines an existing method.
The paper tackled the problem of ambiguous training sample allocation in anchor-based LiDAR 3D object detection by introducing Point Assisted Sample Selection (PASS), which improved average precision to a new state-of-the-art on two datasets.
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.