特征驱动的三维网格模型自适应重采样算法

doi:10.16182/j.issn1004731x.joss.17-0169

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (5): 853-860.doi: 10.16182/j.issn1004731x.joss.17-0169

特征驱动的三维网格模型自适应重采样算法

戴佳佳¹, 范丽鹏^1,2, 庞明勇¹

1. 南京师范大学教育信息工程研究所, 江苏南京 210097;
2.南京理工大学经济管理学院, 江苏南京 210094

收稿日期:2017-04-20 修回日期:2017-07-04 出版日期:2019-05-08 发布日期:2019-11-20
第一作者简介:戴佳佳(1993-),女,安徽芜湖,博士生,研究方向为数字几何处理。
基金资助:
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)

Adaptively Resampling 3D Mesh Models Based on Editable Features

Dai Jiajia¹, Fan Lipeng^1,2, Pang Mingyong¹

1. Institute of EduInfo Science & Engineering, Nanjing Normal University, Nanjing 210097, China;
2. School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China;

Received:2017-04-20 Revised:2017-07-04 Online:2019-05-08 Published:2019-11-20

摘要/Abstract

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。

关键词: 自适应重采样, Ricci流, 模型编辑, 网格参数化

Abstract: 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.

Key words: adaptive sampling, Ricci flow, model editing, mesh parameterization

中图分类号:

TP391

戴佳佳,范丽鹏,庞明勇 . 特征驱动的三维网格模型自适应重采样算法[J]. 系统仿真学报, 2019, 31(5): 853-860.

Dai Jiajia,Fan Lipeng,Pang Mingyong . Adaptively Resampling 3D Mesh Models Based on Editable Features[J]. Journal of System Simulation, 2019, 31(5): 853-860.

参考文献

[1] 赵向军, 路梅, 叶澄清. 全规整重网格化三维模型的压缩[J]. 计算机辅助设计与图形学学报, 2007, 19(4): 436-441.
Zhao Xiangjun, Lu Mei, Ye Chengqing.Mesh compression by uniform resampling[J]. Journal of Computer-Aided Design and Computer Graphics, 2007, 19(4): 436-441.
[2] Ebeida M, Davidson A, Patney A, et al.Efficient maximal Poisson-disk sampling[J]. ACM Trans- actions on Graphics (S0730-0301), 2011, 30(4): 49.
[3] Wu F, Dong W, Kong Y, et al.Feature-aware natural texture synthesis[J]. The Visual Computer (S0178-2789), 2016, 32(1): 43-55.
[4] Wachtel F, Pilleboue A, Coeurjolly D, et al. Fast tile-based adaptive sampling with user-specified Fourier spectra [J]. ACM Transactions on Graphics (S0730-0301), 2014, 33(4): 56:1-56:11.
[5] Taylor J, Bordeaux L, Cashman T, et al.Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences[J]. ACM Transactions on Graphics (S0730-0301), 2016, 35(4): 143.
[6] Ostromoukhov V.Sampling with polyominoes[J]. ACM Transactions on Graphics (S0730-0301), 2007, 26(3): 78.
[7] Dippé M, Wold E.Antialiasing through stochastic sampling[C]//The 12th annual conference on Computer graphics and interactive techniques, New York, ACM, 1985: 69-78.
[8] Cline D, Jeschke S, White K, et al.Dart throwing on surfaces[C]//The Twentieth Eurograp- -hics Conference on Rendering, Girona, Spain, 2009: 1217-1226.
[9] Balzer M, Schlömer T, Deussen O, et al.Capacity-constrained point distributions: a variant of Lloyd's method[J]. ACM Transactions on Graphics (S0730-0301), 2009, 28(6): 86.
[10] Yan D, Guo J, Jia X, et al.Blue-noise remeshing with farthest point optimization[J]. Computer Graphics Forum (S1467-8659), 2014, 33(5): 167-176.
[11] Alliez P, De Verdière É C, Devillers O, et al. Centroidal Voronoi diagrams for isotropic surface remeshing[J]. Graphical Models (S1524-0703), 2005, 67(3): 204-231.
[12] Xu Y, Hu R, Gotsman C, et al.Blue noise sampli- -ng of surfaces[J]. Computers & Graphics (S0097-8493), 2012, 36(4): 232-240.
[13] Chen Z, Yuan Z, Choi Y, et al.Variational blue noise sampling[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2012, 18(10): 1784-1796.
[14] Yan D, Lévy B, Liu Y, et al.Isotropic remeshing with fast and exact computation of restricted Voronoi diagram[J]. Computer Graphics Forum (S1467-8659), 2009, 28(5): 1445-1454.
[15] Yan D, Wonka P.Gap processing for adaptive maximal Poisson-disk sampling[J]. ACM Transactions on Graphics (S0730-0301), 2013, 32(5): 148.
[16] Guo J, Yan D, Jia X, et al.Efficient maximal Poisson-disk sampling and remeshing on surfaces[J]. Computers & Graphics (S0097-8493), 2015, 46(3): 72-79.
[17] Peyré G, Cohen L.Geodesic remeshing using front propagation[J]. International Journal of Computer Vision (S0920-5691), 2006, 69(1): 145-156.
[18] Quinn J, Langbein F, Lai Y, et al.Generalized anisotropic stratified surface sampling[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2013, 19(7): 1143-1157.
[19] 郭凤华, 张彩明, 焦文江. 网格参数化研究进展[J]. 软件学报, 2016, 27(1): 112-135.
Guo Fenghua, Zhang Caiming, Jiao Wenjiang.Research progress on mesh parameterization[J]. Journal of Software, 2016, 27(1): 112-135.
[20] Khodakovsky A, Litke N, Schroder P.Globally smooth parameterizations with low distortion[J]. ACM Transactions on Graphics (S0730-0301), 2003, 22(3): 350-357.
[21] Zeng W, Samaras D, Gu X, et al.Ricci flow for 3D shape analysis[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2010, 32(4): 662-677.
[22] Guo R.Local rigidity of inversive distance circle packing[J]. Transactions of the American Mathematical Society (S0002-9947), 2009, 363(9): 4757-4776.
[23] Meyer M, Desbrun M, Schröder P, et al.Discrete differential-geometry operators for triangulated 2-manifolds[M]. Visualization & Mathematics III. Springer-Verlag, Heidelberg, 2003: 35-57.
[24] Canny J.A computational approach to edge detection[J]. IEEE Transaction on Pattern Analysis & Machine Intelligence (S0162-8828), 1986, 8(6): 679-698.
[25] Gunzburger M.Advances in studies and applications of centroidal Voronoi tessellations[J]. Numerical Mathematics Theory Methods & Applications (S1004-8979), 2010, 3(2): 119-142.
[26] Wang J, Cui C, Rui Y, et al.A parallel algorithm for constructing Voronoi diagrams based on point-set adaptive grouping[J]. Journal of Graphics (S2095-302X), 2014, 26(2): 434-446.
[27] Hateley J, Wei H, Chen L.Fast methods for computing centroidal Voronoi tessellations[J]. Journal of Scientific Computing (S1573-7691), 2015, 63(1): 185-212.

特征驱动的三维网格模型自适应重采样算法

Adaptively Resampling 3D Mesh Models Based on Editable Features

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	黄涛, 张智, 丁玉杰, 陈艳波, 王晶, 张文倩. 考虑动态频率安全与N-k故障的鲁棒应急调度方法[J]. 系统仿真学报, 2025, 37(12): 2981-2993.
[2]	张润昭, 陈艳波, 黄涛, 田昊欣, 强涂奔, 张智. 基于异构负荷特征解析预测的虚拟电厂调度方法[J]. 系统仿真学报, 2025, 37(12): 2994-3006.
[3]	于祥星, 赵艳东, 张宝琳. 基于电涡流NES的海上风机塔架振动控制[J]. 系统仿真学报, 2025, 37(12): 3007-3017.
[4]	李斌, 王于绰. 基于多策略融合的光伏系统故障诊断方法[J]. 系统仿真学报, 2025, 37(12): 3018-3032.
[5]	李孝斌, 胡冰, 尹超, 李波, 马军. 基于时空图卷积的汽车配件供应链需求预测与仿真分析[J]. 系统仿真学报, 2025, 37(12): 3060-3074.
[6]	彭艺, 雷云揆, 杨青青, 李辉, 王健明. 改进PID搜索算法的山地环境无人机路径规划[J]. 系统仿真学报, 2025, 37(12): 3075-3086.
[7]	伍枢珩, 刘永奎, 张霖, 肖莹莹, 王力翚. 基于改进YOLOv8的轻量级装配工件检测算法[J]. 系统仿真学报, 2025, 37(12): 3099-3111.
[8]	陈逸, 邱思航, 朱正秋, 季雅泰, 赵勇, 鞠儒生. 基于启发式的人-大模型协作寻源方法[J]. 系统仿真学报, 2025, 37(12): 3112-3127.
[9]	任亮, 周泽榕, 马云峰. “货到人”系统订单拣选和分拣协同优化问题[J]. 系统仿真学报, 2025, 37(12): 3128-3139.
[10]	索婧怡, 卢柏宏, 屈澈. 影视LED光源光强分布测定及其在游戏引擎中的仿真研究[J]. 系统仿真学报, 2025, 37(12): 3140-3151.
[11]	龚建兴, 胡海, 任海慧, 吴瑞祥. 面向虚实结合的军事训练系统互操作模型与运用[J]. 系统仿真学报, 2025, 37(12): 3161-3175.
[12]	徐智霞, 王蕊, 孙楠, 何兵, 沈晓卫, 朱晓菲. 基于改进遗传算法的协同干扰资源分配问题研究[J]. 系统仿真学报, 2025, 37(12): 3176-3189.
[13]	刘翔, 金乾坤. 基于PAC-Bayes的多目标强化学习A2C算法研究[J]. 系统仿真学报, 2025, 37(12): 3212-3223.
[14]	杨兰英, 李超, 邹海锋, 万江涛, 张仁强, 刘惠, 卢宏. 基于改进蚁群算法与A*算法相融合的机器人路径规划优化[J]. 系统仿真学报, 2025, 37(11): 2956-2965.
[15]	苏筱婷, 张小威, 田义, 李奇, 王帅豪. 星光导航动态仿真场景时序设计方法研究[J]. 系统仿真学报, 2025, 37(11): 2946-2955.