Class balanced Grouping and Sampling for Point Cloud 3D Object Detection
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.
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