Full Text:   <15327>

Summary:  <2640>

CLC number: TP391.72

On-line Access: 2014-01-29

Received: 2013-07-09

Revision Accepted: 2013-11-21

Crosschecked: 2014-01-15

Cited: 6

Clicked: 10785

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.2 P.91-106


A deep learning approach to the classification of 3D CAD models

Author(s):  Fei-wei Qin, Lu-ye Li, Shu-ming Gao, Xiao-ling Yang, Xiang Chen

Affiliation(s):  State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China

Corresponding email(s):   qinfeiwei@zjucadcg.cn, liluye@cad.zju.edu.cn, smgao@cad.zju.edu.cn, xchen@cad.zju.edu.cn, sunny_aday@163.com

Key Words:  CAD model classification, Design reuse, Machine learning, Neural network

Fei-wei Qin, Lu-ye Li, Shu-ming Gao, Xiao-ling Yang, Xiang Chen. A deep learning approach to the classification of 3D CAD models[J]. Journal of Zhejiang University Science C, 2014, 15(2): 91-106.

@article{title="A deep learning approach to the classification of 3D CAD models",
author="Fei-wei Qin, Lu-ye Li, Shu-ming Gao, Xiao-ling Yang, Xiang Chen",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A deep learning approach to the classification of 3D CAD models
%A Fei-wei Qin
%A Lu-ye Li
%A Shu-ming Gao
%A Xiao-ling Yang
%A Xiang Chen
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 2
%P 91-106
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300185

T1 - A deep learning approach to the classification of 3D CAD models
A1 - Fei-wei Qin
A1 - Lu-ye Li
A1 - Shu-ming Gao
A1 - Xiao-ling Yang
A1 - Xiang Chen
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 2
SP - 91
EP - 106
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300185

Model classification is essential to the management and reuse of 3D CAD models. Manual model classification is laborious and error prone. At the same time, the automatic classification methods are scarce due to the intrinsic complexity of 3D CAD models. In this paper, we propose an automatic 3D CAD model classification approach based on deep neural networks. According to prior knowledge of the CAD domain, features are selected and extracted from 3D CAD models first, and then preprocessed as high dimensional input vectors for category recognition. By analogy with the thinking process of engineers, a deep neural network classifier for 3D CAD models is constructed with the aid of deep learning techniques. To obtain an optimal solution, multiple strategies are appropriately chosen and applied in the training phase, which makes our classifier achieve better performance. We demonstrate the efficiency and effectiveness of our approach through experiments on 3D CAD model datasets.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Bai, J., Gao, S., Tang, W., et al., 2010. Design reuse oriented partial retrieval of CAD models. Comput.-Aided Des., 42(12):1069-1084.

[2]Barutcuoglu, Z., DeCoro, C., 2006. Hierarchical shape classification using Bayesian aggregation. IEEE Int. Conf. on Shape Modeling and Applications, p.44-48.

[3]Bengio, Y., 2009. Learning deep architectures for AI. Found. Trends Mach. Learn., 2(1):1-127.

[4]Bengio, Y., Courville, A., Vincent, P., 2013. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell., 35(8):1798-1828.

[5]Bergstra, J., Breuleux, O., Bastien, F., et al., 2010. Theano: a CPU and GPU math compiler in Python. Proc. 9th Python in Science Conf., p.1-7.

[6]Bimbo, A.D., Pala, P., 2006. Content-based retrieval of 3D models. ACM Trans. Multim. Comput. Commun. Appl., 2(1):20-43.

[7]Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford University Press, Oxford.

[8]Bordes, A., Glorot, X., Weston, J., et al., 2014. A semantic matching energy function for learning with multi-relational data. Mach. Learn., 94(2):233-259.

[9]Chen, D., 2003. Three-Dimensional Model Shape Description and Retrieval Based on Light Field Descriptors. PhD Thesis, National Taiwan University, Taiwan.

[10]Chen, D., Tian, X., Shen, Y., et al., 2003. On visual similarity based 3D model retrieval. Comput. Graph. Forum, 22(3):223-232.

[11]Dreiseitl, S., Ohno-Machado, L., 2002. Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform., 35(5):352-359.

[12]Duda, R.O., Hart, P.E., Stork, D.G., 2001. Pattern Classification (2nd Ed.). John Wiley & Sons, New York.

[13]Fang, X., Luo, H., Tang, J., 2005. Structural damage detection using neural network with learning rate improvement. Comput. & Struct., 83(25-26):2150-2161.

[14]Glorot, X., Bengio, Y., 2010. Understanding the difficulty of training deep feedforward neural networks. Proc. Int. Conf. on Artificial Intelligence and Statistics, p.249-256.

[15]Gunn, T.G., 1982. The mechanization of design and manufacturing. Sci. Am., 247:114-130.

[16]Haykin, S., 2008. Neural Networks and Learning Machines (3rd Ed.). Prentice Hall, New York.

[17]Hinton, G.E., Salakhutdinov, R.R., 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507.

[18]Hou, S., Lou, K., Ramani, K., 2005. SVM-based semantic clustering and retrieval of a 3D model database. Comput. Aided Des. Appl., 2(1-4):155-164.

[19]Huang, F.J., LeCun, Y., 2006. Large-scale learning with SVM and convolutional nets for generic object categorization. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.284-291.

[20]Ip, C.Y., Regli, W.C., 2005a. Content-based classification of CAD models with supervised learning. Comput. Aided Des. Appl., 2(5):609-617.

[21]Ip, C.Y., Regli, W.C., 2005b. Manufacturing classification of CAD models using curvature and SVMs. Int. Conf. on Shape Modeling and Applications, p.361-365.

[22]Ip, C.Y., Regli, W.C., Sieger, L., et al., 2003. Automated learning of model classifications. Proc. 8th ACM Symp. on Solid Modeling and Applications, p.322-327.

[23]Iyer, N., Jayanti, S., Lou, K., et al., 2005. Three-dimensional shape searching: state-of-the-art review and future trends. Comput.-Aided Des., 37(5):509-530.

[24]Kavukcuoglu, K., Sermanet, P., Boureau, Y., et al., 2010. Learning convolutional feature hierarchies for visual recognition. Proc. 24th Annual Conf. on Neural Information Processing Systems, p.1090-1098.

[25]Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. ImageNet classification with deep convolutional neural networks. Proc. 26th Annual Conf. on Neural Information Processing Systems, p.1106-1114.

[26]Larochelle, H., Bengio, Y., Louradour, J., et al., 2009. Exploring strategies for training deep neural networks. J. Mach. Learn. Res., 10(1):1-40.

[27]Ngiam, J., Chen, Z., Koh, P.W., et al., 2011. Learning deep energy models. Proc. 28th Int. Conf. on Machine Learning, p.1105-1112.

[28]Prechelt, L., 1998. Automatic early stopping using cross validation: quantifying the criteria. Neur. Networks, 11(4):761-767.

[29]Ranzato, M.A., Mnih, V., Hinton, G.E., 2010. Generating more realistic images using gated MRF’s. Proc. 24th Annual Conference on Neural Information Processing Systems, p.2002-2010.

[30]Reed, R., 1993. Pruning algorithms—a survey. IEEE Trans. Neur. Networks, 4(5):740-747.

[31]Shilane, P., Min, P., Kazhdan, M., et al., 2004. The Princeton Shape Benchmark. Proc. Conf. on Shape Modeling Applications, p.167-178.

[32]Wang, W., Liu, X., Liu, L., 2013. Shape matching and retrieval based on multiple feature descriptors. Comput. Aided Draft. Des. Manuf., 23(1):60-67.

[33]Wei, W., Yang, Y., Lin, J., et al., 2008. Color-based 3D model classification using Hopfield neural network. Proc. Int. Conf. on Computer Science and Software Engineering, p.883-886.

[34]Wu, M.C., Jen, S.R., 1996. A neural network approach to the classification of 3D prismatic parts. Int. J. Adv. Manuf. Technol., 11(5):325-335.

[35]Yao, Y., Rosasco, L., Caponnetto, A., 2007. On early stopping in gradient descent learning. Constr. Approx., 26(2):289-315.

[36]Zhang, D., Lu, G., 2002. An integrated approach to shape based image retrieval. Proc. 5th Asian Conf. on Computer Vision, p.652-657.

Open peer comments: Debate/Discuss/Question/Opinion


editor@No address<No mail>

2014-01-29 19:26:10

Reviewer: This paper proposed a method for automatically classifying CAD models based on deep neural networks. It combines light field descriptor (LFD) and Zernike moments descriptor to construct a deep network classifier then all trainable parameters for the 3D CAD model classifier are trained. Experimental results have shown the applicability of the proposed method.

This work is the first one to my knowledge to apply the technique of deep learning in the classification of 3D models. The algorithm is technically sound and the results are good.

Deep learning has been widely studied and used in image community. I am curious why this technique is used in 3D content until now. And it is nice to see that this technique does work well for 3D shape classification.

Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE