한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제25권10호
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  • Pages.1452-1455
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  • 2021
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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생성적 적대 신경망 기반 3차원 포인트 클라우드 향상 기법

3D Point Cloud Enhancement based on Generative Adversarial Network

  • Moon, HyungDo (Institute Of Applied Hologram, Wonkwang University) ;
  • Kang, Hoonjong (Department of Electronic Engineering, Wonkwang University) ;
  • Jo, Dongsik (School of IT Convergence, University of Ulsan)
  • 투고 : 2021.08.31
  • 심사 : 2021.09.15
  • 발행 : 2021.10.31
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초록

Recently, point clouds are generated by capturing real space in 3D, and it is actively applied and serviced for performances, exhibitions, education, and training. These point cloud data require post-correction work to be used in virtual environments due to errors caused by the capture environment with sensors and cameras. In this paper, we propose an enhancement technique for 3D point cloud data by applying generative adversarial network(GAN). Thus, we performed an approach to regenerate point clouds as an input of GAN. Through our method presented in this paper, point clouds with a lot of noise is configured in the same shape as the real object and environment, enabling precise interaction with the reconstructed content.

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과제정보

This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP), grant funded by the Korean government(MSIT) (No. 2020-0-00226, Development of High-Definition, Unstructured Plenoptic video Acquisition Technology for Medium and Large Space).

참고문헌

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