3D object detection is a crucial problem in environmental perception for autonomous driving. Currently, most works focused on LiDAR, camera, or their fusion, while very few algorithms involve a RaDAR sensor, especially 4D RaDAR providing 3D position and velocity information. 4D RaDAR can work well in bad weather and has a higher performance than traditional 3D RaDAR, but it also contains lots of noise information and suffers measurement ambiguities. Existing 3D object detection methods can't judge the heading of objects by focusing on local features in sparse point clouds. To better overcome this problem, we propose a new method named RPFA-Net only using a 4D RaDAR, which utilizes a self-attention mechanism instead of PointNet to extract point clouds' global features. These global features containing long-distance information can effectively improve the network's ability to regress the heading angle of objects and enhance detection accuracy. Our method's performance is enhanced by 8.13% of 3D mAP and 5.52% of BEV mAP compared with the baseline. Extensive experiments show that RPFA-Net surpasses state-of-the-art 3D detection methods on Astyx HiRes 2019 dataset. The code and pre-trained models are available at https://github.com/adept-thu/RPFA-Net.git.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    RPFA-Net: a 4D RaDAR Pillar Feature Attention Network for 3D Object Detection


    Contributors:
    Xu, Baowei (author) / Zhang, Xinyu (author) / Wang, Li (author) / Hu, Xiaomei (author) / Li, Zhiwei (author) / Pan, Shuyue (author) / Li, Jun (author) / Deng, Yongqiang (author)


    Publication date :

    2021-09-19


    Size :

    944045 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    SCNet3D: Rethinking the Feature Extraction Process of Pillar-Based 3D Object Detection

    Li, Junru / Wang, Zhiling / Gong, Diancheng et al. | IEEE | 2025


    Channel Boosting Feature Ensemble for Radar-based Object Detection

    Azam, Shoaib / Munir, Farzeen / Jeon, Moongu | IEEE | 2021



    Consecutive Feature Network for Object Detection

    Huang, Jiaming / Lan, Xiaosong / Li, Shuxiao et al. | British Library Conference Proceedings | 2018