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<p>The reliability and cost-effectiveness of 4D millimeter wave radar in adverse weather conditions make it an indispensable auxiliary sensor in autonomous driving. In response, we propose PSTOPS, a novel and effective 3D object detection framework based on 4D radar. Current 3D detectors based on 4D radar typically employ 3D convolutional backbones. However, because of their limited receptive fields, these backbones cannot efficiently capture large-scale contextual information, which is crucial for object detection. To address this issue, we adapt a single-step, window-based pillar transformer backbone. This backbone leverages the self-attention mechanism to achieve long-range relationships between pillars while effectively utilizing the sparsity of radar point clouds, naturally avoiding substantial computational overhead. Additionally, we found that during training, center-based label assignment often fails to generate sufficient positive samples, and anchor-based label allocation often suffers from imbalance when dealing with objects of different scales. To solve these problems, we designed a dynamic cross-label detection head. This head dynamically assigns positive samples for each object from a cross-shaped region, ensuring an adequate and balanced number of positive samples during training. Although only using 4D imaging radar, PSTOPS achieves performance comparable to the methods that fuse 4D radar and cameras.We evaluate the proposed method PSTOPS on the dataset View-of-Delft (VoD). Our method achieves 50.99% for the Entire Annotated Area and 71.55% for the Region of Interest. This demonstrates that 4D radar possesses significant potential for 3D object detection applications.</p>

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Pillar-Based Adaptive Sparse Transformer with Cost-Optimized Positive Sample Selection for 4D Radar Object Detection

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Tongzhou Chen,
  • Danfeng Wu,
  • Fenfen Zhou,
  • Hui Jing,
  • Minchi Kuang,
  • Xueyan Zhang

摘要

The reliability and cost-effectiveness of 4D millimeter wave radar in adverse weather conditions make it an indispensable auxiliary sensor in autonomous driving. In response, we propose PSTOPS, a novel and effective 3D object detection framework based on 4D radar. Current 3D detectors based on 4D radar typically employ 3D convolutional backbones. However, because of their limited receptive fields, these backbones cannot efficiently capture large-scale contextual information, which is crucial for object detection. To address this issue, we adapt a single-step, window-based pillar transformer backbone. This backbone leverages the self-attention mechanism to achieve long-range relationships between pillars while effectively utilizing the sparsity of radar point clouds, naturally avoiding substantial computational overhead. Additionally, we found that during training, center-based label assignment often fails to generate sufficient positive samples, and anchor-based label allocation often suffers from imbalance when dealing with objects of different scales. To solve these problems, we designed a dynamic cross-label detection head. This head dynamically assigns positive samples for each object from a cross-shaped region, ensuring an adequate and balanced number of positive samples during training. Although only using 4D imaging radar, PSTOPS achieves performance comparable to the methods that fuse 4D radar and cameras.We evaluate the proposed method PSTOPS on the dataset View-of-Delft (VoD). Our method achieves 50.99% for the Entire Annotated Area and 71.55% for the Region of Interest. This demonstrates that 4D radar possesses significant potential for 3D object detection applications.