摘要
针对目前网格简化仅依赖局部特征而造成对尖锐特征保持差的问题,提出一种新的基于视觉显著度加权的简化算法。算法首先通过Voronoi内外极点与采样点之间的关系来计算离散曲面局部极点特征值,然后叠加不同尺度下局部特征值的高斯差分获得特征的视觉显著度。在进行网格简化时,将该显著度作为权重赋值给每个点的二次误差矩阵,从而达到对显著度较高区域特征保持的目的。实验结果表明,所提出的简化算法与传统基于局部曲率的算法相比能够更有效地保持原始网格固有几何特性,特别是对于视觉较为敏感的尖锐特征。
In this paper,we represent a novel method for mesh simplification.Compared with conventional methods that are based only on local features,our method exerts visual saliency feature as the weight to sample points during simplification,hence has better performance on persevering visual features.Firstly,the method computes the local feature values by the relationship between Voronoi-poles and the sample points.Then the global visual saliency features are computed by overlaying the Difference of Gaussian of local feature values get under different scales.Finally,the computed global visual saliency features are used as the weight to the quadric error metrics,in order to hold the positions where have of higher global visual saliency during iteration of the mesh simplification.The simplified results show that our algorithm not only gets better visual feature preservation but also smaller qualified errors comparing to state of the art methods.
作者
魏宁
徐婷婷
高开源
董方敏
WEI Ning;XU Tingting;GAO Kaiyuan;DONG Fangmin(Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University,Yichang Hubei 443002, China)
出处
《图学学报》
CSCD
北大核心
2017年第3期314-319,共6页
Journal of Graphics
基金
国家重点研发计划项目(2016YFC0802503)
国家自然科学基金项目(61272237
61272236)
