This report presents our method which wins the nuScenes 3D 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 grouping head to boost the performance for the categories with similar shapes. Based on the Challenge results, our method outperforms the PointPillars [14] baseline by a large margin across all metrics, achieving state-of-the-art (SOTA) detection performance on the nuScenes dataset. Code will be released at CBGS.

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