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Two-Stage Multimodal Fusion Networks Based on Virtual Point Clouds
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Teng Cheng1, 2, 3, Hao Ni1, 2, 3, Qiang Zhang1, 2, 3, 4, Wenchong Wang4, Qin Shi1, 2, 3
Automotive Engineering | 2024, 46(2) : 222 - 229
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Automotive Engineering | 2024, 46(2): 222-229
• Feature Topic：Key Technologies on Intelligent and Connected Vehicles •
Two-Stage Multimodal Fusion Networks Based on Virtual Point Clouds
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Teng Cheng1, 2, 3, Hao Ni1, 2, 3, Qiang Zhang1, 2, 3, 4, Wenchong Wang4, Qin Shi1, 2, 3
Affiliations
  • 1. Hefei University of Technology，Key Laboratory for Automated Vehicle Safety Technology of Anhui Province，Hefei 230009
  • 2. Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province，Hefei 230000
  • 3. School of Automotive and Transportation Engineering，Hefei University of Technology，Hefei 230000
  • 4. Chery Automobile Co. ，Ltd. ，Wuhu 241000
Published: 2024-02-25 doi: 10.19562/j.chinasae.qcgc.2024.02.004
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To address the impact of sparsity and disorder of point clouds on target detection accuracy，a two-stage multimodal fusion network VPC-VoxelNet based on virtual point clouds is proposed in this paper. Firstly，virtual point clouds are constructed using image detection target information to increase the density of point clouds，thus improving the performance of target features. Secondly，the dimensionality of point cloud features is increased to distinguish real and virtual point clouds，and a voxel with confidence encoding is used to enhance the correlation of point clouds. Finally，the scale factor of the virtual point clouds is adopted to design the loss function to increase the supervised training of image detection and improve the training efficiency of the two-stage network，and avoid the cumulative model error problem of the two-stage end-to-end network model. The target detection network，VPC-VoxelNet，is tested on the KITTI dataset，and the detection accuracy is better than that of the classical 3-dimensional point cloud detection network and certain multi-sensor information fusion networks，with a vehicle detection accuracy of 86.9%.

target detection  /  multimodal perception  /  virtual point cloud  /  loss function
Teng Cheng, Hao Ni, Qiang Zhang, Wenchong Wang, Qin Shi. Two-Stage Multimodal Fusion Networks Based on Virtual Point Clouds[J]. Automotive Engineering, 2024 , 46 (2) : 222 -229 . DOI: 10.19562/j.chinasae.qcgc.2024.02.004
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.02.004
  • Receive Date：2023-05-10
  • Online Date：2025-07-20
  • Published：2024-02-25
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History
  • Received：2023-05-10
  • Revised：2023-07-30
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Affiliations
    1. Hefei University of Technology，Key Laboratory for Automated Vehicle Safety Technology of Anhui Province，Hefei 230009
    2. Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province，Hefei 230000
    3. School of Automotive and Transportation Engineering，Hefei University of Technology，Hefei 230000
    4. Chery Automobile Co. ，Ltd. ，Wuhu 241000
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.02.004
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