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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: 6
Clicked: 10840
A deep learning approach to the classification of 3D CAD models
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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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author="Fei-wei Qin, Lu-ye Li, Shu-ming Gao, Xiao-ling Yang, Xiang Chen",
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doi="10.1631/jzus.C1300185"
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J0 - Journal of Zhejiang University Science C
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DOI - 10.1631/jzus.C1300185
Abstract: 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.
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Open peer comments: Debate/Discuss/Question/Opinion
<1>
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.