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

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

Benjin Zhu , Zhengkai Jiang , Xiangxin Zhou , Zeming Li , Gang Yu This is my paper · ORCID
classification cs.CV
keywords detectionclass-balancedautonomousbalancedchallengedrivingobjectperformance
verification ladder T0 review T1 audit T2 compute T3 formal
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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 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. NOVA: Next-step Open-Vocabulary Autoregression for 3D Multi-Object Tracking in Autonomous Driving

    cs.CV 2026-03 conditional novelty 6.0 of 10

    A 0.5B LLM associates open-vocabulary 3D detections via trajectory sequence completion, raising novel-category AMOTA on nuScenes from 2.2% to 22.4%.

  3. CRAB: Camera-Radar Fusion for Reducing Depth Ambiguity in Backward Projection based View Transformation

    cs.CV 2025-09 conditional novelty 6.0 of 10

    CRAB uses radar occupancy and radar context features to reduce depth ambiguity in backward projection camera-to-BEV transformation, achieving 62.4% NDS and 54.0% mAP on nuScenes.

  4. VESPA: Towards un(Human)supervised Open-World Pointcloud Labeling for Autonomous Driving

    cs.CV 2025-07 conditional novelty 6.0 of 10

    VESPA fuses LiDAR geometry with vision-language model semantics to generate open-vocabulary 3D pseudolabels, achieving 52.95% class-agnostic AP and 46.54% 3-class mAP on nuScenes without human supervision.

  5. COME: Adding Scene-Centric Forecasting Control to Occupancy World Model

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    COME adds a scene-centric forecasting branch as a ControlNet-style condition to a diffusion occupancy world model, improving static-scene consistency and beating prior methods on Occ3D-nuScenes while hiding a stronger...

  6. Collaborative Perceiver: Elevating Vision-based 3D Object Detection via Local Density-Aware Spatial Occupancy

    cs.CV 2025-07 reject novelty 5.0 of 10

    A multi-task camera model that adds local-density-aware occupancy prediction to 3D object detection reports strong nuScenes scores, but internal inconsistencies and missing code prevent confirmation.

  7. DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos

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    DySS combines state-space feature learning with dynamic query merging and pruning to improve both accuracy and speed for camera-based 3D detection on nuScenes.

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