摘要
提出一种由可编辑特征驱动的三维网格模型自适应重采样算法,该算法运用一组特征曲线控制重采样密度。将网格模型参数化到平面域,用灰度几何图像表示原模型的局部几何信息;由几何图像的灰度及用户编辑信息定义三维模型表面采样的密度控制函数;该函数控制采样点在参数域中的疏密分布,并采用重心Voronoi方法优化采样点的局部分布;将采样结果映射到三维空间生成重采样模型。算法能有效地处理不同模型,得到的重采样点分布具有局部特征自适应性,用户以交互方式控制采样分布。
We propose a hybrid algorithm for adaptively resampling 3D triangulations by user-defined editable features.The method parameterizes a 3D mesh model into 2D parameter plane,and the geometric properties of the original model is calculated and represented on a planar domain.According to a constructed geometric image of the original model and user-defined editing information,the method creates a global density function for the resampled model.The sampling density function is employed to control distribution of samples in the 2D parameter domain.The method uses centroidal Voronoi tessellation technique to further optimize local distribution of the sampled points.The created samples in 2D domain are mapped to 3D space and the resulted model is obtained with adaptive sampling property.Experiments show that the algorithm can deal with various mesh models efficiently and robustly.The distribution of vertices of resulted model is adaptive and can be controlled by user-defined features.
作者
戴佳佳
范丽鹏
庞明勇
Dai Jiajia;Fan Lipeng;Pang Mingyong(Institute of EduInfo Science&Engineering,Nanjing Normal University,Nanjing 210097,China;School of Economics and Management,Nanjing University of Science&Technology,Nanjing 210094,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2019年第5期853-860,共8页
Journal of System Simulation
基金
国家自然科学基金重点项目(41631175)
江苏省社会科学基金(15TQB005)
江苏省现代教育技术研究课题(2014-R33356)
