Recently, 3D object detection techniques based on the fusion of camera and LiDAR sensor modalities have received much attention due to their complementary capabilities. How-ever, prevalent multi-modal models are relatively homogeneous in terms of feature fusion strategies, making their performance being strictly limited to the detection results of one of the modalities. While the latest data-level fusion models based on virtual point clouds do not make further use of image features, resulting in a large amount of noise in depth estimation. To address the above issues, this paper integrates the advantages of data-level and feature-level sensor fusion, and proposes MLF3D, a 3D object detection based on multi-level fusion. MLF3D generates virtual point clouds to realize the data-level fusion, and implements feature-level fusion through two key designs: VIConv3D and ASFA. VIConv3D reduces the noise problem and realizes deep interactive enhancement of features through cross-modal fusion, noise sensing, and cross-space fusion. ASFA refines the bounding box by adaptively fusing cross-layer spatial semantic information. Our MLF3D achieves 92.91%, 87.71% AP and 85.25% AP in easy, medium and hard scenarios on the KITTI’s 3D Car Detection Leaderboard, realizing excellent performance.


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    Titel :

    MLF3D: Multi-Level Fusion for Multi-Modal 3D Object Detection


    Beteiligte:
    Jiang, Han (Autor:in) / Wang, Jianbin (Autor:in) / Xiao, Jianru (Autor:in) / Zhao, Yanan (Autor:in) / Chen, Wanqing (Autor:in) / Ren, Yilong (Autor:in) / Yu, Haiyang (Autor:in)


    Erscheinungsdatum :

    02.06.2024


    Format / Umfang :

    2769536 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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