| Title |
BIM Mesh Optimization Algorithm Using K-Nearest Neighbors for Augmented Reality Visualization |
| Authors |
빠빠윈아웅(Pa Pa Win Aung) ; 이동환(Lee, Donghwan) ; 박주영(Park, Jooyoung) ; 조민건(Cho, Mingeon) ; 박승희(Park, Seunghee) |
| DOI |
https://doi.org/10.12652/Ksce.2022.42.2.0249 |
| Keywords |
메쉬 경량화; k-최근접이웃 (KNN); 빌딩 정보 모델링 (BIM); 증강현실 (AR) Mesh optimization; K-nearest neighbors (KNN); Building information modeling (BIM); Augmented reality (AR) |
| Abstract |
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. |