Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: bayes-an