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Acta Optica Sinica, Volume. 45, Issue 8, 0815003(2025)

Contour Feature-Enhanced 3D Perception Method for Atypical Semantic Object

Farong Kou1、*, Kan Wang2, Yajun Zhao2, Tianxiang Yang2, and Gengyi Lü2
Author Affiliations
  • 1Institute of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi , China
  • 2College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
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    References (26)
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    Figures & Tables(14)
    Structural diagram of SCE-FBOcc

    Fig. 1. Structural diagram of SCE-FBOcc

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    Contour convolution module

    Fig. 2. Contour convolution module

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    Framework of contour encoding attention module

    Fig. 3. Framework of contour encoding attention module

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    Cross-scale semantic contour attention module

    Fig. 4. Cross-scale semantic contour attention module

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    Contour comparison diagram

    Fig. 5. Contour comparison diagram

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    Framework of contour semantic assisted learning module

    Fig. 6. Framework of contour semantic assisted learning module

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    Visualization results of Occupancy

    Fig. 7. Visualization results of Occupancy

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    Visualization comparison of depth features

    Fig. 8. Visualization comparison of depth features

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    Local comparison chart

    Fig. 9. Local comparison chart

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    Comparison chart of occupancy grids

    Fig. 10. Comparison chart of occupancy grids

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    Comparison Chart of Depth Estimation

    Fig. 11. Comparison Chart of Depth Estimation

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    • Table 1. Campare with other method

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      Table 1. Campare with other method

      ClassBEVDetBEVFormer-OccCTFOccFBOcc*SCE-BEVDetSCE-FBOcc
      Barrier15.2937.8739.9242.8515.6144.83
      Cons. vehicle1.357.3516.9621.132.3423.36
      Manmade14.5120.6220.6236.1715.7535.96
      Traffic cone0.1321.8122.7225.730.1827.64
      Pedestrian0.4321.6322.725.780.7327.94
      Car13.5742.4342.2446.9814.8747.94
      Truck7.4730.731.1335.798.6535.17
      Bus5.1840.4438.3944.265.4242.13
      Trailer4.5822.3822.9831.115.1334.44
      Bicycle4.1217.9820.5626.734.3228.55
      Motorcycle0.224.0824.5226.170.3328.56
      Dri. sur52.7655.3253.4278.2353.2379.41
      Vegetation15.2517.7118.134.4116.4735.98
      mIoU10.3727.7128.7836.5611.7737.83

    • Table 2. Ablation study

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      Table 2. Ablation study

      ModelBarrierCons. vehicleManmadeTraffic coneDri. surmIoU
      No42.8521.1336.1725.7378.2340.82
      CEAM43.5122.2335.1225.9178.0240.95
      CEAM+SCALM43.2422.3135.8925.8379.2341.30
      CEAM+SCALM+CSCAM44.8323.3635.9627.6479.4142.24

    • Table 3. Interfering latency study

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      Table 3. Interfering latency study

      ModuleParams /106Model params /106FLOPS /109Latency /msOverall latency /ms
      FPN+CM DepthNet27.3267.80105.935.6697.21
      CADN （PyTorch）31.8572.33119.4112.22103.75
      CADN （CUDA Ops）31.8572.33119.419.12100.53
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    Farong Kou, Kan Wang, Yajun Zhao, Tianxiang Yang, Gengyi Lü. Contour Feature-Enhanced 3D Perception Method for Atypical Semantic Object[J]. Acta Optica Sinica, 2025, 45(8): 0815003

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    Paper Information

    Category: Machine Vision

    Received: Dec. 20, 2024

    Accepted: Feb. 28, 2025

    Published Online: Apr. 27, 2025

    The Author Email: Farong Kou (koufarong@xust.edu.cn)

    DOI:10.3788/AOS241916

    CSTR:32393.14.AOS241916

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology