• Laser & Optoelectronics Progress
  • Vol. 57, Issue 20, 201508 (2020)
Xunhua Liu1、2、*, Shaoyuan Sun1、2, Lipeng Gu1、2, and Xiang Li1、2
Author Affiliations
  • 1College of Information Science and Technology, Donghua University, Shanghai 201620, China
  • 2Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China;
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    DOI: 10.3788/LOP57.201508 Cite this Article Set citation alerts
    Xunhua Liu, Shaoyuan Sun, Lipeng Gu, Xiang Li. 3D Object Detection Based on Improved Frustum PointNet[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201508 Copy Citation Text show less
    Improved F-PointNet structure
    Fig. 1. Improved F-PointNet structure
    Network structure for extracting candidate regions of frustum point cloud
    Fig. 2. Network structure for extracting candidate regions of frustum point cloud
    Registration results of 2D images and 3D point clouds. (a) RGB image; (b) 3D point cloud data; (c) registration effect of Fig. (a) and Fig. (b)
    Fig. 3. Registration results of 2D images and 3D point clouds. (a) RGB image; (b) 3D point cloud data; (c) registration effect of Fig. (a) and Fig. (b)
    3D target frustum candidate region initially obtained
    Fig. 4. 3D target frustum candidate region initially obtained
    Schematic of viewing frustum orientation adjustment
    Fig. 5. Schematic of viewing frustum orientation adjustment
    3D target mask prediction network
    Fig. 6. 3D target mask prediction network
    Attention mechanism implementation process
    Fig. 7. Attention mechanism implementation process
    3D target bounding box prediction network
    Fig. 8. 3D target bounding box prediction network
    Coordinate transformation of target instance point cloud
    Fig. 9. Coordinate transformation of target instance point cloud
    Visual 3D target bounding box prediction results. (a) 2D target detection result; (b) 3D target detection result
    Fig. 10. Visual 3D target bounding box prediction results. (a) 2D target detection result; (b) 3D target detection result
    ItemCPUComputing memoryGPUSystemCUDA
    ContentIntel i5-66008 GBNVIDIA GTX 1070Ubuntu 16.04CUDA 9.0
    Table 1. Experimental configuration
    xmarginCarPedestrianCyclist
    EasyModerateHardEasyModerateHardEasyModerateHard
    082.0568.4662.4265.9458.3550.8774.1055.5452.09
    0.182.3969.5362.5261.9055.2049.0273.4555.4652.26
    0.282.7970.8563.4967.0559.1651.8276.0457.0953.33
    0.383.1970.5963.1365.0657.5350.5973.5555.7652.73
    Table 2. AP values of 3D target detection under each threshold unit: %
    PartAP /%
    Wide-threshold mask(xmargin=0.2)Attention mechanismFocal LossEasyModerateHard
    ---82.0568.4662.42
    --82.7970.8563.49
    --81.8969.2362.54
    --82.7369.8963.27
    83.0471.2563.82
    Table 3. Influence of each processing part on AP values
    MethodAP /%
    EasyModerateHard
    MV3D[4]71.2962.2856.56
    F-PointNet[5]82.0568.4662.42
    UberATG-ContFuse[14]82.5466.2264.04
    MLOD[15]72.2464.2057.20
    Proposed83.0471.2563.82
    Table 4. Comparison of AP values of different models
    Xunhua Liu, Shaoyuan Sun, Lipeng Gu, Xiang Li. 3D Object Detection Based on Improved Frustum PointNet[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201508
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