基于圆形参数域和重要性采样的三维模型网格重建  

3D Model Remeshing Using Circular Parameter Domain and Importance Sampling

作　　者：付妍[1] 朱晓明[1] 周秉锋[1] 

机构地区：[1]北京大学计算机科学技术研究所,北京100871

出　　处：《计算机学报》2007年第12期2124-2131,共8页Chinese Journal of Computers

基　　金：国家自然科学基金(60573149)资助~~

摘　　要：给出了一个基于参数化和重要性采样的网格重建算法.算法搜索一条切线将三维模型表面切分成一片碟形表面,并将其参数化到二维单位圆中.根据模型的曲率信息和参数化的扭曲度生成一张控制图.利用蓝噪声采样算法对用控制图调制后的采样密度采样得到离散采样点.通过对采样点进行平面三角化并将其映射回三维空间实现了三维网格重建.实验证明,该方法有效地改善了参数化的扭曲度,并能得到具有自适应性的高度规则网格.A 3D model remeshing algorithm based on circular parameter domain and importance sampling is proposed in this paper. The algorithm automatically searches a cut path to cut the model into a disk-like surface and then parameterizes it into a circular domain. Based on the curvature of the mesh and the stretch introduced by the parameterization, a control map is defined to control the sampling distribution. The control map is then sampled by a hierarchical blue noise important sampling. The generated sample points are triangulated and mapped to 3D space. As a result, we obtain a high-quality adaptive mesh whose vertex distribution is determined by the density of the control map.

关 键 词：网格重建 重要性采样 网格参数化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]