International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Object Detection with LiDAR Point Cloud and RGBD Synthesis Using GNN

  • Jung, Tae-Won (Department of Realistic Convergence Contents KwangWoon University Graduate School) ;
  • Jeong, Chi-Seo (Department of Information System KwangWoon University Graduate School) ;
  • Lee, Jong-Yong (Ingenium College of liberal arts, Kwangwoon University) ;
  • Jung, Kye-Dong (Ingenium College of liberal arts, Kwangwoon University)
  • Received : 2020.07.24
  • Accepted : 2020.08.05
  • Published : 2020.09.30
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Abstract

The 3D point cloud is a key technology of object detection for virtual reality and augmented reality. In order to apply various areas of object detection, it is necessary to obtain 3D information and even color information more easily. In general, to generate a 3D point cloud, it is acquired using an expensive scanner device. However, 3D and characteristic information such as RGB and depth can be easily obtained in a mobile device. GNN (Graph Neural Network) can be used for object detection based on these characteristics. In this paper, we have generated RGB and RGBD by detecting basic information and characteristic information from the KITTI dataset, which is often used in 3D point cloud object detection. We have generated RGB-GNN with i-GNN, which is the most widely used LiDAR characteristic information, and color information characteristics that can be obtained from mobile devices. We compared and analyzed object detection accuracy using RGBD-GNN, which characterizes color and depth information.

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References

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