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CLC number: TP391.72

On-line Access: 2014-01-29

Received: 2013-07-09

Revision Accepted: 2013-11-21

Crosschecked: 2014-01-15

Cited: 5

Clicked: 2247

Citations:  Bibtex RefMan EndNote GB/T7714

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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.

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A1 - Xiang Chen
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PB - Zhejiang University Press & Springer
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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


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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.

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