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arxiv: 1908.09492 · v1 · pith:DEQLVIXK · submitted 2019-08-26 · cs.CV

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

Benjin Zhu , Zhengkai Jiang , Xiangxin Zhou , Zeming Li , Gang Yu This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:DEQLVIXKrecord.jsonopen to challenge →

classification cs.CV
keywords detectionclass-balancedautonomousbalancedchallengedrivingobjectperformance
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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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Cited by 13 Pith papers

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