Earticle

초록

영어

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 RGBDGNN, which characterizes color and depth information.

목차

Abstract
1. Introduction
2. Related research
2.1 3D point cloud
2.2 Graph Neural Network
3. Data Preprocessing for Supervised Learning
4. Experimental Results
5. Conclusion
References

키워드

저자

• Tae-Won Jung [ Doctoral Student, Department of Realistic Convergence Contents KwangWoon University Graduate School ]
• Chi-Seo Jeong [ Master Student, Department of Information System KwangWoon University Graduate School ]
• Jong-Yong Lee [ Professor, Ingenium College of liberal arts, Kwangwoon University ]
• Kye-Dong Jung [ Professor, Ingenium College of liberal arts, Kwangwoon University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380