This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019). Generally, we utilize sparse 3D convolution to extract rich semantic features, which are then fed into a class-balanced multi-head network to perform 3D object detection. To handle the severe class imbalance problem inherent in the autonomous driving scenarios, we design a class-balanced sampling and augmentation strategy to generate a more balanced data distribution. Furthermore, we propose a balanced group-ing head to boost the performance for the categories withsimilar shapes. Based on the Challenge results, our methodoutperforms the PointPillars [14] baseline by a large mar-gin across all metrics, achieving state-of-the-art detection performance on the nuScenes dataset. Code will be released at CBGS.

该研究报告介绍了我们的方法，该方法利用稀疏3D卷积提取丰富的语义特征，并将其输入到一个类平衡的多头网络中进行三维物体检测。通过我们设计的样本策略，可以在自动驾驶场景中处理严重的类别不平衡问题，并且在nuScenes数据集上，我们通过平衡分类组头进一步提高了性能。在nuScenes3D Detection Challenge（WAD，CVPR 2019）中，我们的方法优于PointPillars [14]基线，在所有指标上取得了最新的检测性能。

点云三维目标检测中的平衡分组和采样