대한토목학회논문집 (KSCE Journal of Civil and Environmental Engineering Research)

  • 제42권2호
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  • Pages.249-256
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  • 2022
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  • 1015-6348(pISSN)
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  • 2799-9629(eISSN)
대한토목학회 (Korean Society of Civil Engeneers)
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증강현실 시각화를 위해 K-최근접 이웃을 사용한 BIM 메쉬 경량화 알고리즘

BIM Mesh Optimization Algorithm Using K-Nearest Neighbors for Augmented Reality Visualization

  • 빠 빠 윈 아웅 (성균관대학교 글로벌스마트시티융합공학과) ;
  • 이동환 (성균관대학교 미래도시융합공학과) ;
  • 박주영 (성균관대학교 건설환경시스템공학과) ;
  • 조민건 (성균관대학교 미래도시융합공학과) ;
  • 박승희 (성균관대학교 건설환경공학부)
  • 투고 : 2021.12.04
  • 심사 : 2021.12.30
  • 발행 : 2022.04.01
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초록

최근 BIM (Building Information Modeling)과 AR (Augmented Reality)을 결합한 실시간 시각화 기술이 건설관리 의사 결정 및 처리 효율성을 높이는 데 도움이 된다는 것을 보여주기 위한 다양한 연구가 활발히 진행되고 있다. 그러나, 대용량 BIM 데이터는 AR에 적용할 경우 데이터 전송 문제, 이미지 단절, 영상 끊김 등과 같은 다양한 문제가 발생함으로 3차원(3D) 모델의 메쉬 최적화를 통해 시각화의 효율성을 향상시켜야 한다. 대부분의 기존 메쉬 경량화 방법은 복잡하고 경계가 많은 3D 모델의 메쉬를 적절하게 처리할 수 없다. 이에 본 연구에서는 고성능 AR 시각화를 위해 BIM 데이터를 재구성하기 위한 k-최근접이웃(KNN) 분류 프레임워크 기반 메쉬 경량화 알고리즘을 제안하였다. 제안 알고리즘은 선정된 BIM 모델을 삼각형 중심 개념 기반의 Unity C# 코드로 경량화하였고 모델의 데이터 세트를 활용하여 정점 사이의 거리를 정의할 수 있는 KNN로 분류되었다. 그 결과 전체 모델과 각 구조의 경량화 메쉬 점 및 삼각형 개수가 각각 약 56 % 및 약 42 % 감소됨을 확인할 수 있었다. 결과적으로, 원본 모델과 비교했을 때 경량화한 모델은 시각적인 요소 및 정보 손실이 없었고, 따라서, AR 기기 활용 시 고성능 시각화를 향상시킬 수 있을 것으로 기대된다.

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

키워드

과제정보

This research was conducted with the support of the "National R&D Project for Smart Construction Technology (No.21SMIP-A158708-02)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation and supported by 「Innovative Talent Education Program for Smart City」. This manuscript is a revised version of the 2021 CONVENTION paper.

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