Non-rigid 3D Shape Classification Based on Low-Level Features
Non-rigid 3D shape classification is an important issue in digital geometry processing. In this paper, we propose a novel non-rigid 3D shape classification method using Convolutional Neural Networks (CNNs) based on the scale-invariant heat kernel signature (SIHKS). Firstly, SIHKS feature is extracted and we can get a matrix for every 3D shape. Then CNNs is employed to shape classification. The matrix of 3D shapes can be the input of CNNs. Finally, we can obtain the category probability of 3D shapes. Experimental results demonstrate the proposed method can get better results compared with SVM.
KeywordsNon-rigid 3D shape classification Low-level feature Scale-invariant heat kernel signature (SIHKS) Convolutional neural networks
This work was partially supported by Beijing Natural Science Foundation (4162019).
- 1.J.W.H. Tangelder, R.C. Veltkamp, A survey of content based 3D shape retrieval methods, in Proceedings of International Conference on Shape Modeling Applications (2004), pp. 145–156Google Scholar
- 3.R. Jain, J. Tyagi, S.K. Singh et al., Hybrid context aware recommender systems, in Advance-ment in Mathematical Sciences: Proceedings of the, International Conference on Recent Advances in Mathematical Sciences and ITS Applications, pp. 020–028 (2017)Google Scholar
- 4.P.S. Wang, Y. Liu, Y.X. Guo et al., O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. 36(4), 72 (2017)Google Scholar
- 8.R.M. Rustamov, Laplace-Beltrami eigenfunctions for deformation invariant shape representation, in Proceedings of the fifth Eurographics symposium on Geometry processing (Eurographics Association, 2007), pp. 225–233Google Scholar
- 9.R.M. Rustamov, Template based shape descriptor, in Proceedings of the 2nd Eurographics conference on 3D Object Retrieval (Eurographics Association, 2009), pp. 1–7Google Scholar
- 11.M.M. Bronstein, I. Kokkinos, Scale-invariant heat kernel signatures for non-rigid shape recognition. Comput. Vis. Pattern Recog (IEEE), 1704–1711 (2010)Google Scholar
- 12.M. Aubry, U. Schlickewei, D. Cremers, The wave kernel signature: a quantum mechanical approach to shape analysis, in IEEE International Conference on Computer Vision Work-shops (IEEE, 2011), pp. 1626–1633Google Scholar
- 13.Z. Li, D. Wang, L. Boyang et al., 3D model classification using salient features for content representation, in International Conference on Natural Computation (IEEE, 2010), pp. 3541–3545Google Scholar
- 14.F.W. Qin, L.I. Lu-Ye, S.M. Gao et al., A deep learning approach to the classification of 3D CAD models. Front. Inf. Technol. Electr. Eng. 15(2), 91–106 (2014)Google Scholar
- 17.Z. Lian, A. Godil, T. Fabry et al., SHREC’10 track: non-rigid 3D shape retrieval, in Eurographics Workshop on 3D Object Retrieval, Norrköping, Sweden, 2 May 2010, pp. 101–108Google Scholar