Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Real-time 3D Volumetric Model Generation using Multiview RGB-D Camera

다시점 RGB-D 카메라를 이용한 실시간 3차원 체적 모델의 생성

  • 김경진 (광운대학교 전자재료공학과) ;
  • 박병서 (광운대학교 전자재료공학과) ;
  • 김동욱 (광운대학교 전자재료공학과) ;
  • 권순철 (광운대학교 스마트시스템학과) ;
  • 서영호 (광운대학교 전자재료공학과)
  • Received : 2020.03.26
  • Accepted : 2020.05.04
  • Published : 2020.05.30
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Abstract

In this paper, we propose a modified optimization algorithm for point cloud matching of multi-view RGB-D cameras. In general, in the computer vision field, it is very important to accurately estimate the position of the camera. The 3D model generation methods proposed in the previous research require a large number of cameras or expensive 3D cameras. Also, the methods of obtaining the external parameters of the camera through the 2D image have a large error. In this paper, we propose a matching technique for generating a 3D point cloud and mesh model that can provide omnidirectional free viewpoint using 8 low-cost RGB-D cameras. We propose a method that uses a depth map-based function optimization method with RGB images and obtains coordinate transformation parameters that can generate a high-quality 3D model without obtaining initial parameters.

본 논문에서는 다시점 RGB-D 카메라의 포인트 클라우드 정합을 위한 수정된 최적화 알고리즘을 제안한다. 일반적으로 컴퓨터 비전 분야에서는 카메라의 위치를 정밀하게 추정하는 것은 매우 중요하다. 기존의 연구에서 제안된 3D 모델 생성 방식들은 많은 카메라 대수나 고가의 3차원 Camera를 필요로 한다. 또한 2차원 이미지를 통해 카메라 외부 파라미터를 얻는 방식들은 큰 오차를 가지고 있다. 본 논문에서는 저가의 RGB-D 카메라를 8개 사용하여 전방위 자유시점을 제공할 수 있는 3차원 포인트 클라우드 및 매쉬 모델을 생성하기 위한 정합 기법을 제안하고자 한다. RGB영상과 함께 깊이지도 기반의 함수 최적화 방식을 이용하고, 초기 파라미터를 구하지 않으면서 고품질의 3차원 모델을 생성할 수 있는 좌표 변환 파라미터를 구하는 방식을 제안한다.

Keywords

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