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: during down-sampling stages, and 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.

中文翻译：

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并非所有点都是平衡的：来自点云的类平衡单级室外多类 3D 物体检测器

室外3D物体检测是自动驾驶领域的热门话题。主流的纯点云方法是通过不同的面向任务的策略进行下采样，以保留代表性的前景点。尽管此类策略有利于查找实例，但这些方法仍然存在两个问题：在下采样阶段和最终保留的点云中。前者的不平衡导致小物体的精度较差；而后者忽略了背景点，导致误报现象。为了解决不平衡现象，我们提出了一种简单而有效的平衡3D检测器，称为CB-SSD，包括两种平衡策略：类平衡策略（CBS）和前景/背景平衡策略（FBBS）。值得注意的是，我们不会改变点云的分布。相反，我们将模型的注意力平等地引导到不同的类别。 CB-SSD 对小物体表现出更好的精度，减少了前景点和背景点相似的误报。考虑到速度和准确性，CB-SSD 在 KITTI 和 ONCE 数据集上基于纯点云（单阶段）实现了最先进的技术。在 KITTI 上，CB-SSD 在 81 FPS 下获得了 72.92 mAP 的多类精度。