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PCDR-DFF: multi-modal 3D object detection based on point cloud diversity representation and dual feature fusion

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Abstract

Recently, multi-modal 3D object detection techniques based on point clouds and images have received increasing attention. However, existing methods for multi-modal feature fusion are often relatively singular, and single point cloud representation methods also have some limitations. For example, voxelization may result in the loss of fine-grained information, while 2D images lack depth information, which can restrict the accuracy of detection. Therefore, in this work, we propose a novel method for multi-modal 3D object detection based on point cloud diversity representation and dual feature fusion, PCDR-DFF, to improve the prediction accuracy of 3D object detection. Firstly, point clouds are projected to the image coordinate system and extract multi-level features of the point cloud corresponding to the image using a 2D backbone network. Then, the point clouds are jointly characterized using graphs and pillars, and the 3D features of the point clouds are extracted using graph neural networks and residual connectivity. Finally, a dual feature fusion method is designed to improve the accuracy of detection with the help of a well-designed multi-point fusion model and multi-feature fusion mechanism embedded with a spare 3D-U Net. Extensive experiments on the KITTI dataset demonstrate the effectiveness and competitiveness of our proposed models in comparison with other methods.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62102003), Natural Science Foundation of Anhui Province (2108085QF258), Anhui Postdoctoral Science Foundation (2022B623), the University Synergy Innovation Program of Anhui Province (GXXT-2021-006, GXXT-2022-038), Central guiding local technology development special funds (202107d06020001), the Institute of Energy, Hefei Comprehensive National Science Center under (21KZS217), University-level general projects of Anhui University of science and technology (xjyb2020-04).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China

    Chenxing **a, Xubing Li, Bin Ge & **an** Fang

  2. Institute of Energy, Hefei Comprehensive National Science Center, Hefei, Anhui, China

    Chenxing **a & Ke Yang

  3. Anhui Purvar Bigdata Technology Co. Ltd, Huainan, 232001, China

    Chenxing **a

  4. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui, China

    **uju Gao

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  6. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    **an** Fang

  7. The School of Electronics and Information Engineering, Anhui University, Hefei, Anhui, China

    Yan Zhang

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**a, C., Li, X., Gao, X. et al. PCDR-DFF: multi-modal 3D object detection based on point cloud diversity representation and dual feature fusion. Neural Comput & Applic 36, 9329–9346 (2024). https://doi.org/10.1007/s00521-024-09561-w

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