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• W2969987486 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." @default.
• W2969987486 created "2019-08-29" @default.
• W2969987486 creator A5003400275 @default.
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• W2969987486 date "2019-08-26" @default.
• W2969987486 modified "2023-09-23" @default.
• W2969987486 title "Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection" @default.
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• W2969987486 doi "https://doi.org/10.48550/arxiv.1908.09492" @default.
• W2969987486 hasPublicationYear "2019" @default.
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