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EPGNet: Enhanced Point Cloud Generation for 3D Object Detection.

Qingsheng Chen1, Cien Fan1, Weizheng Jin1

  • 1School of Electronic Information, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|December 9, 2020
PubMed
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This study introduces a novel method for 3D object detection using point cloud data by leveraging object symmetry to complete missing information. This approach enhances detection accuracy for autonomous driving systems.

Area of Science:

  • Computer Vision
  • Robotics
  • 3D Data Processing

Background:

  • 3D object detection from point clouds is crucial for autonomous driving.
  • Lidar scanning limitations result in incomplete object structures.
  • Existing methods often overlook object symmetry, a key structural prior.

Purpose of the Study:

  • To improve 3D object detection by incorporating object symmetry.
  • To address the challenge of incomplete point cloud data due to lidar scanning.
  • To develop a framework that utilizes symmetry for enhanced point cloud completion and detection.

Main Methods:

  • A two-stage detection framework is proposed.
  • An encoder-decoder structure generates symmetry points to complete the point cloud.
Keywords:
3D objection detectionautonomous drivingenhanced point cloudsymmetry

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  • An enhanced point cloud is fed into an anchor-based region proposal network for detection.

    • Main Results:

    • The method effectively completes missing object parts in point clouds.
    • Experiments on the KITTI benchmark demonstrate improved performance.
    • Superior results were achieved in both 3D and Bird's Eye View (BEV) object detection.

    Conclusions:

    • Leveraging object symmetry significantly enhances 3D object detection from point clouds.
    • The proposed two-stage framework offers a robust solution for incomplete lidar data.
    • This method shows state-of-the-art performance on challenging autonomous driving benchmarks.