As a key component of the environmental awareness system of autonomous vehicles, efficient and accurate 3D detection of obstacles is directly related to driving safety. However, in complex and ever-changing driving environments, single sensory input like camera or LiDAR often fails to provide accurate and comprehensive environmental information. To improve the effectiveness of 3D object detection, this paper focuses on a multi-perception feature fusion strategy and proposes a supervised learning method called MPF-Net. Firstly, the image features are mapped into a 3-dimensional view cone space, then fused with the LiDAR features in a unified bird's-eye-view (BEV) space, finally an efficient BEV network is designed for 3D object detection. The performance has been validated on KITTI database and compared with several other models, the results demonstrate that the proposed method has more accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A 3D Object Detection Method Based on Cross-Attention Multimodal Feature Fusion


    Contributors:
    Tian, Juanxiu (author) / Zhou, Yanping (author) / Peng, Meng (author) / Chen, Xudong (author)


    Publication date :

    2024-10-25


    Size :

    2514976 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection

    Xu, Shaoqing / Zhou, Dingfu / Fang, Jin et al. | IEEE | 2021



    Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection

    Sun, Huawei / Feng, Hao / Stettinger, Georg et al. | IEEE | 2023


    Multimodal target detection algorithm based on adaptive feature fusion

    Li, Yitong / He, Chuchao / Di, Ruohai et al. | SPIE | 2024


    Anti-Occlusion UAV Target Detection Based on Attention Feature Fusion

    Zhu, Xiaoyong / Luo, Cai / Lv, Xinrong et al. | IEEE | 2023