Not all points are balanced: Class balanced single-stage outdoor multi-class 3D object detector from point clouds

Abstract

Outdoor 3D object detection is a hot topic in autonomous driving. The mainstream pure point cloud method is down-sampling through different task-oriented strategies to retain representative foreground points. Although such strategies are conducive to finding instances, these methods still suffer from two issues: class points imbalance during down-sampling stages, and foreground/background points imbalance in the final retained point clouds. The former imbalance results in poor precision for small objects; and the latter ignores background points, leading to a false positive phenomenon. To tackle the unbalanced phenomenon, we propose a simple yet effective balanced 3D detector, termed CB-SSD, including two balanced strategies: class balance strategy (CBS) and foreground/background balance strategy (FBBS). It is important to note that we do not alter the distribution of point clouds. Instead, we guide the model’s attention towards different classes equally. CB-SSD shows better precision on small objects, reducing false positives where foreground points and background points are similar. Considering both speed and accuracy, CB-SSD achieves state-of-the-art based on pure point clouds (single-stage) on KITTI and ONCE datasets. On KITTI, CB-SSD attains a multi-class accuracy of 72.92 mAP with 81 FPS.