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arXiv · 1908.09492

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

Benjin Zhu·Zhengkai Jiang·Xiangxin Zhou·Zeming Li·Gang Yu
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Abstract

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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Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, Gang Yu. 2019-08-26. Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection. https://arxiv.org/abs/1908.09492

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