基于曲度特征的三维模型检索算法

doi:10.11772/j.issn.1001-9081.2016.07.1914

计算机应用 ›› 2016, Vol. 36 ›› Issue (7): 1914-1917.DOI: 10.11772/j.issn.1001-9081.2016.07.1914

基于曲度特征的三维模型检索算法

周继来¹, 周明全^1,2, 耿国华¹, 王小凤¹

1. 西北大学信息科学与技术学院, 西安 710127;
2. 北京师范大学信息科学与技术学院, 北京 100875

收稿日期:2015-12-17 修回日期:2016-03-30 出版日期:2016-07-10 发布日期:2016-07-14
通讯作者: 周明全
作者简介:周继来(1977-),男,四川达州人,博士研究生,主要研究方向:三维可视化、图形图像处理、模式识别;周明全(1954-),男,陕西临潼人,教授,博士生导师,博士,主要研究方向:计算机可视化、生物特征识别、中文信息处理;耿国华(1955-),女,山东莱西人,教授,博士生导师,博士,主要研究方向:智能信息处理、可视化分析;王小凤(1979-),女,陕西蒲城人,副教授,博士,主要研究方向:基于内容的多媒体处理和检索。
基金资助:
国家自然科学基金资助项目（61373117）；陕西省教育厅基金资助项目（12JK0730）。

3D model retrieval algorithm based on curvedness feature

ZHOU Jilai¹, ZHOU Mingquan^1,2, GENG Guohua¹, WANG Xiaofeng¹

1. School of Information Science and Technology, Northwest University, Xi'an Shaanxi 710127, China;
2. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China

Received:2015-12-17 Revised:2016-03-30 Online:2016-07-10 Published:2016-07-14
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61373117), the Science Foundation of Education Ministry of Shaanxi Province (12JK0730).

摘要/Abstract

摘要： 针对如何提高复杂曲面的三维模型的检索精度的问题，提出了一种基于曲度特征的三维模型检索算法。首先，在模型表面选取随机采样点，计算点所在局部曲面的高斯曲率和平均曲率，通过高斯曲率和平均曲率求出随机点的曲度值，曲度值表明了曲面的凹凸属性。然后，以模型的质心为球心，以随机点与质心距离和曲度值为坐标轴建立坐标系，统计出一定距离范围内曲度值分布的概率，构建距离与曲度的分布矩阵，以此分布矩阵作为三维模型特征描述符。该特征描述符具有旋转不变性和平移不变性，能够很好地反映复杂曲面的几何特征。最后，通过比较分布矩阵给出不同模型间的相似度。实验结果表明，该方法相比形状分布算法的检索性能有较大提高，尤其适用于具有复杂曲面的三维模型检索。

关键词: 特征提取, 曲度, 高斯曲率, 平均曲率, 三维模型检索

Abstract: To improve the retrieval precision of 3D model with the complex surface, a new method based on curvedness feature was proposed. First, the sample points were obtained on the 3D model surface. The curvedness of these points was obtained by computing Gauss curvature and Mean curvature. The curvedness values showed properties of 3D model surface. Secondly, the centroid of the model was set as the center. The coordinate system in which two coordinate axes were the curvedness value and the Euclid distance between the random point and the center was constructed. The distribution matrix of curvedness feature was obtained by computing the statistical number of the sample points in the different Euclid distance. This distribution matrix was the feature descriptor of the 3D model. This descriptor had the property of rotation invariance and translation invariance, which could well reflect the geometric characteristics of complex surfaces. Finally, the similarity between different models was given by comparing the curvedness distribution matrix. The experimental results show that the proposed method can effectively improve the accuracy of the 3D model retrieval, especially suitable for those models with complex surfaces.

Key words: feature extraction, curvedness, Gaussian curvature, mean curvature, 3D model retrieval

中图分类号:

TP391.72

周继来, 周明全, 耿国华, 王小凤. 基于曲度特征的三维模型检索算法[J]. 计算机应用, 2016, 36(7): 1914-1917.

ZHOU Jilai, ZHOU Mingquan, GENG Guohua, WANG Xiaofeng. 3D model retrieval algorithm based on curvedness feature[J]. Journal of Computer Applications, 2016, 36(7): 1914-1917.

参考文献

[1] 霍星,谭结庆.利用特征向量的三维模型检索[J].工程图学学报,2009,30(3):76-79.(HUO X,TAN J Q.3D model retrieval based on feature vector[J]. Journal of Engineering Graphics, 2009, 30(3):76-79.)
[2] 徐士彪,车武军,张晓鹏.基于形状特征的三维模型检索技术综述[J].中国体视学与图像分析,2010,159(4):439-450.(XU S B, CHE W J,ZHANG X P. A survey of 3D model retrieval based on shape features[J]. Chinese Journal of Stereology and Image Analysis, 2010,159(4):439-450.)
[3] OSADA R, FUNKHOUSER T, CHAZELLE B, et al. Shape distributions[J].ACM Transactions on Graphics, 2002, 21(4):807-832.
[4] GAO Y, DAI Q H, ZHANG N Y. 3D model comparison using spatial structure circular descriptor[J]. Pattern Recognition, 2010, 43(3):1142-1151.
[5] 蒋立军,张旭堂,张广玉.基于面积分布算子的三维模型检索算法[J].计算机工程与应用,2012,48(12):6-13.(JIANG L J, ZHANG X T, ZHANG G Y. 3D model retrieval method based on area distributions[J]. Computer Engineering and Applications, 2012, 48(12):6-13.)
[6] SHIH J L, CHEN H Y. A 3D model retrieval approach using the interior and exterior 3D shape information[J]. Multimedia Tools and Applications, 2009, 43(1):45-62.
[7] 周明全,樊亚春,耿国华.一种基于空间对称变换的三维模型形状描述方法[J].电子学报,2010,38(4):853-859.(ZHOU M Q, FAN Y C, GENG G H. A spatial symmetry descriptor for 3D model[J]. Acta Electronica Sinica, 2010, 38(4):853-859.)
[8] 朱新懿,耿国华.使用形状变化描述三维模型[J].计算机应用研究,2015,32(3):922-925.(ZHU X Y, GENG G H. Shape variation representation of 3D shape descriptor[J]. Application Research of Computers, 2015, 32(3):922-925.)
[9] TORKHANI F, WANG K, CHASSERY J M. A curvature-tensor based perceptual quality metric for 3D triangular meshes[J]. Machine Graphics & Vision, 2013, 32(16):1-25.
[10] CHEN Q, YU Y M. 3D CAD model retrieval based on feature fusion[J]. Advanced Materials Research, 2013, 765/766/767:316-319.
[11] LIU Y J, ZHANG X D, LI Z M, et al. Extended cone-curvature based salient points detection and 3D model retrieval[J]. Multimedia Tools and Applications, 2013, 64(3):671-693.
[12] DESBRUN M, MEYER M, SCHRODER P, et al. Implicit fairing of irregular meshes using diffusion and curvature flow[C]//SIGGRAPH'99:Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. New York:ACM, 1999:317-324.
[13] 方惠兰,王国瑾.三角网格曲面上离散曲率估算方法的比较与分析[J].计算机辅助设计与图形学学报,2005,17(11):17-23.(FANG H L, WANG G J. Comparison and analysis of discrete curvatures estimation methods for triangular meshes[J]. Journal of Computer-Aided Design & Computer Graphics, 2005, 17(11):17-23.)
[14] 吴大任.微分几何讲义[M].北京:人民教育出版社,1984:126-128.(WU D R. Differential Geometry[M]. Beijing:People's Education Press, 1984:126-128.)
[15] 屠宏,耿国华.一种基于局部特征的三维模型检索算法[J].计算机工程,2015,41(3):218-222.(TU H, GENG G H. A 3D model retrieval algorithm based on local feature[J]. Computer Engineering, 2015, 41(3):218-222.)
[16] DEJIAN V, VRANI C, DIETMAR S. An improvement of rotation invariant 3D shape descriptor based on functions on concentric sphere[C]//Proceedings of the 2003 IEEE International Conference on Image Processing. Piscataway, NJ:IEEE, 2003:757-760.
[17] SHILANE P, MIN P, KAZHDAN M, et al. The Princeton shape benchmark[C]//SMI'04:Proceedings of the 2004 International Conference on Shape Modeling and Applications. Washington, DC:IEEE Computer Society, 2004:167-178.
[18] OHBUCHI R, MINAMITANI T, TAKEI T. Shape-similarity search of 3D models by using enhanced shape functions[C]//TPCG'03:Proceedings of the Theory and Practice of Computer Graphics 2003. Washington, DC:IEEE Computer Society, 2005:97-104.

基于曲度特征的三维模型检索算法

3D model retrieval algorithm based on curvedness feature

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