Three-Dimensional Model Similarity Analysis Based on Salient Features Spectral Embedding
HAN Li1,2, YAN Zhen1, XU Jian-Guo1, TANG Di1
1.School of Computer and Information Technology, Liaoning Normal University, Dalian 116081 2.Faculty of Computer Science and Technology, Dalian University of Technology, Dalian 116024
Abstract：Aiming at the requirement of efficient 3D model retrieval technology, a three-dimensional model similarity analysis based on salient features spectral embedding method is proposed. Firstly, the salient features are extracted by curvature-based method and a convex-concave measurement to build the salient features representation for the shape. Then these features are embedded in a spectral domain to reveal the intrinsic shape characteristics based on Laplacian Eigenmap. Finally, combined with the thin plate splines method, the model similarity analysis and registration are implemented. The experimental results show that by using the proposed method shape matching is implemented efficiently and the consistent structural features in same category models are identified. Moreover, it is robust to the imperfect shape matching.
韩丽颜震徐建国唐棣. 基于显著特征谱嵌入的三维模型相似性分析*[J]. 模式识别与人工智能, 2015, 28(12): 1119-1126.
HAN Li , YAN Zhen , XU Jian-Guo , TANG Di. Three-Dimensional Model Similarity Analysis Based on Salient Features Spectral Embedding. , 2015, 28(12): 1119-1126.
[1] Chen Y, Medioni G. Object Modeling by Registration of Multiple Range Image // Proc of the IEEE International Conference on Robotics and Automation. Sacramento, USA, 1991, III: 2724-2729 [2] Mitra N J, Gelfand N, Pottmann H, et al. Registration of Point Cloud Data from a Geometric Optimization Perspective // Proc of the 2nd Eurographics Symposium on Geometry Processing. Nice, France, 2004: 22-31 [3] Gelfand N, Ikemoto L, Rusinkiewicz S, et al. Geometrically Stable Sampling for the ICP Algorithm // Proc of the 4th International Conference on 3-D Digital Imaging and Modeling. Banff, Canada, 2003: 260-267 [4] Mémoli F, Sapiro G. A Theoretical and Computational Framework for Isometry Invariant Recognition of Point Cloud Data. Foundations of Computational Mathematics, 2005, 5(3): 313-347 [5] Zheng Q, Sharf A, Tagliasacchi A, et al. Consensus Skeleton for Non-rigid Space-Time Registration. Computer Graphics Forum, 2010, 29(2): 635-644 [6] Jain V, Zhang H. Robust 3D Shape Correspondence in the Spectral Domain // Proc of the IEEE International Conference on Shape Modeling and Applications. Matsushima, Japan, 2006: 19-30 [7] Gelfand N, Guibas L J. Shape Segmentation Using Local Slippage Analysis // Proc of the 2nd Eurographics Symposium on Geometry Processing. Nice, France, 2004: 214-223 [8] Meyer M, Desbrun M, Schrder P, et al. Discrete Differential-Geometry Operators for Triangulated 2-Manifolds // Hege H C, Polthier K, eds. Visualization and Mathematics III. Berlin, Germany: Springer, 2003: 35-57 [9] Hilaga M, Shinagawa Y, Kohmura T, et al. Topology Matching for Fully Automatic Similarity Estimation of 3D Shapes // Proc of the 28th Annual Conference on Computer Graphics. Los Angeles, USA, 2001: 203-212 [10] Liu R, Zhang H. Segmentation of 3D Meshes through Spectral Clustering // Proc of the 12th Pacific Conference on Computer Graphics and Applications. Seoul, Korea, 2004: 298-305 [11] Han L, Gao X S, Chu B Z. Discrete Curvature Constrained Tri-angle Mesh Model Segmentation Technique. Journal of Computer-Aided Design & Computer Graphics, 2009, 21(6): 831-835 (in Chinese) (韩 丽,高小山,楚秉智.离散曲率约束的三角网格模型拓扑分割算法.计算机辅助设计与图形学学报, 2009, 21(6): 831-835) [12] Han L, Li L, Xu J G, et al. Structure Analysis for Human Models Based on Surface and Spatial Features. Pattern Recognition and Artificial Intelligence, 2015, 28(3): 231-238 (in Chinese) (韩 丽,黎 琳,徐建国,等.基于表面及空间特征的人体模型结构分析.模式识别与人工智能, 2015, 28(3): 231-238) [13] Donato G, Belongie S. Approximate Thin Plate Spline Mappings // Proc of the 7th European Conference on Computer Vision. Copenhagen, Denmark, 2002, II: 531-542 [14] Chui H L, Rangarajan A. A New Algorithm for Non-rigid Point Matching // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2000, II: 44-51 [15] Bookstein F L. Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(6): 567-585 [16] Zinber T, Schmidt J, Niemann H. A Refined ICP Algorithm for Robust 3D Correspondence Estimation // Proc of the International Conference on Image Processing. Barcelona, Spain, 2003, II: 695-698 [17] Belongie S, Malik J, Puzicha J. Shape Matching and Object Re-cognition Using Shape Contexts. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509-522 [18] Sun D M, Qiu Z D. A New Non-rigid Image Matching Algorithm Using Thin-Plate Spline. Acta Electronica Sinica, 2002, 30(8): 1104-1107 (in Chinese) (孙冬梅,裘正定.利用薄板样条函数实现非刚性图像匹配算法.电子学报, 2002, 30(8): 1104-1107) [19] Tang J, Wang N, Liang D, et al. Laplacian Spectral Correspondence Using TPS Deformation Model. Journal of System Simu-lation, 2008, 20(4): 862-866, 904 (in Chinese) (唐 俊,王 年,梁 栋,等.一种使用TPS变形模型的Laplace谱匹配算法.系统仿真学报, 2008, 20(4): 862-866, 904) [20] Cai J J, Kong L F, Li H T. Study on 2D Joint Points Calibration of Human Based on Euclidean Distance Transform. Computer Simulation, 2012, 29(7): 243-246 (in Chinese) (蔡建军,孔令富,李海涛.基于欧式距离变换的人体2D关节点标定.计算机仿真, 2012, 29(7): 243-246) [21] Faloutsos C, Lin K I. FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets // Proc of the ACM SIGMOD International Conference on Management of Data. San Jose, USA, 1995: 163-174
2015, Vol. 28 
