CAJ | 학술논문

由于视线方向上的网格单元前后相互影响,导致3D流场可视化面临遮挡和混乱问题,为此提出一种基于流场特征的多分辨率绘制方法.首先利用基于GPU的BP网络流场特征提取方法对流场典型特征或用户关注的新特征进行选取、训练和识别;在此基础上,利用Voronoi图技术对特征数据构造特征树;最后基于鱼眼视图多分辨率技术进行绘制.对绘制和性能进行测试的实验结果表明,该方法能有效地提取流场特征,降低遮挡和混乱对可视化效果影响.
유우시선방향상적망격단원전후상호영향,도치3D류장가시화면림차당화혼란문제,위차제출일충기우류장특정적다분변솔회제방법.수선이용기우GPU적BP망락류장특정제취방법대류장전형특정혹용호관주적신특정진행선취、훈련화식별;재차기출상,이용Voronoi도기술대특정수거구조특정수;최후기우어안시도다분변솔기술진행회제.대회제화성능진행측시적실험결과표명,해방법능유효지제취류장특정,강저차당화혼란대가시화효과영향.
Due to the mutual influence of grid points on the view direction, visualization to a 3D fluid field often has problem of occlusion and cluttering. In this paper, we propose a novel approach for solving the occlusion and cluttering problem based on multi-resolution rendering. By the solution, first, a new fluid feature extraction method is presented by taking advantage of the strong non-linear ability of the neural network. Then based on the flow feature detected, a Voronoi graph method is used to organize field data. Finally, the field data is visualized by the "fisheye views" method. The test result of the visualization effects and performance to our method shows that our proposed approach is feasible and efficient in solving the occlusion and cluttering problem.