방송공학회논문지 (Journal of Broadcast Engineering)

  • 제25권5호
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  • Pages.798-807
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  • 2020
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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Multi-view Stereo에서 Dense Point Cloud를 위한 Fusing 알고리즘

Fusing Algorithm for Dense Point Cloud in Multi-view Stereo

  • 한현덕 (세종대학교 전자정보통신공학과) ;
  • 한종기 (세종대학교 전자정보통신공학과)
  • 투고 : 2020.08.28
  • 심사 : 2020.09.17
  • 발행 : 2020.09.30
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초록

디지털 카메라와 휴대폰 카메라의 발달로 인해 이미지를 기반으로 3차원 물체를 복원하는 기술이 크게 발전했다. 하지만 Structure-from-Motion(SfM)과 Multi-view Stereo(MVS)를 이용한 결과인 dense point cloud에는 여전히 듬성한 영역이 존재한다. 이는 깊이 정보를 추정하는데 있는 어려움과, 깊이 지도를 point cloud로 fusing할 때 이웃 영상과의 깊이 정보가 불일치할 경우 깊이 정보를 삭제하고 point를 생성하지 않았기 때문이다. 본 논문에선 평면을 모델링하여 삭제된 깊이 정보에 새로운 깊이 정보를 부여하고 point를 생성하여 기존 결과보다 dense한 point cloud를 생성하는 알고리즘을 제안한다. 실험 결과를 통해 제안하는 알고리즘이 효과적으로 기존의 방법보다 dense한 point cloud를 생성함을 확인할 수 있다.

As technologies using digital camera have been developed, 3D images can be constructed from the pictures captured by using multiple cameras. The 3D image data is represented in a form of point cloud which consists of 3D coordinate of the data and the related attributes. Various techniques have been proposed to construct the point cloud data. Among them, Structure-from-Motion (SfM) and Multi-view Stereo (MVS) are examples of the image-based technologies in this field. Based on the conventional research, the point cloud data generated from SfM and MVS may be sparse because the depth information may be incorrect and some data have been removed. In this paper, we propose an efficient algorithm to enhance the point cloud so that the density of the generated point cloud increases. Simulation results show that the proposed algorithm outperforms the conventional algorithms objectively and subjectively.

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