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

Authors :
Zhu, Benjin
Jiang, Zhengkai
Zhou, Xiangxin
Li, Zeming
Yu, Gang
Publication Year :
2019
Publisher :
arXiv, 2019.

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.<br />Comment: technical report

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....847d7e54f481b5137da57a74c7878798
Full Text :
https://doi.org/10.48550/arxiv.1908.09492