  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.
