한국컴퓨터그래픽스학회논문지 (Journal of the Korea Computer Graphics Society)

  • 제23권1호
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  • Pages.57-65
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  • 2017
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  • 1975-7883(pISSN)
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  • 2383-529X(eISSN)
한국컴퓨터그래픽스학회 (Korea Computer Graphics Society)
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3차원 형태 특징의 사전 학습을 이용한 기하 복원

Geometry Reconstruction Using Dictionary Learning of 3D Shape Features

  • 황정민 (세종대학교 컴퓨터공학과) ;
  • 윤여진 (세종대학교 컴퓨터공학과) ;
  • 최수미 (세종대학교 컴퓨터공학과)
  • Hwang, Jung-Min (Department of Computer Engineering, Sejong University) ;
  • Yoon, Yeo-Jin (Department of Computer Engineering, Sejong University) ;
  • Choi, Soo-Mi (Department of Computer Engineering, Sejong University)
  • 투고 : 2017.02.10
  • 심사 : 2017.03.07
  • 발행 : 2017.03.07
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초록

본 논문에서는 포인트 클라우드로 구성된 모델 내의 오류를 줄이고, 기하학적 형태를 복원하기 위한 사전 학습 방법을 제시한다. 이를 위해, 대상 모델과 유사한 형태 특징을 갖는 모델로부터 3차원 특징 정보를 추출하여 사전을 구성하고, 이를 통해 기하 복원을 수행한다. 본 연구에서 제시한 방법은 다음과 같이 세 단계로 구성된다. 첫째, 유사 모델로부터 기하 패치를 구성하는 단계, 둘째, 획득한 패치의 3차원 형태 특징을 학습하는 단계, 셋째, 학습된 사전을 이용하여 기하를 복원하는 단계이며, 최종적으로 원본 모델과 복원 결과의 오차를 계산하며, 복원 결과의 정확도를 확인한다.

In this paper, we present a dictionary learning method for reducing errors in point cloud models and reconstructing their geometry. For this, 3D feature information is extracted from the models which have a similar shape characteristic as the target model. Then a dictionary is constructed and the geometry is reconstructed using the dictionary. The presented method in this paper consists of the following three steps. First, a geometric patch is constructed from a similar model. Second, a morphological 3D feature of the acquired patch is learned. Third, a geometry reconstruction is performed using the learned dictionary. Finally, the error between the original model and the reconstruction result is calculated, and the accuracy of the reconstruction result is checked.

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과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

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