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| A deep learning approach to the classification of 3D CAD models |
| Fei-wei Qin, Lu-ye Li, Shu-ming Gao, Xiao-ling Yang, Xiang Chen |
| State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China |
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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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Received: 09 July 2013
Published: 29 January 2014
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用于三维CAD模型分类的深度学习方法
研究目的：模型分类对于工程领域中三维CAD模型的有效管理和重用非常关键。由于三维CAD模型分类固有的复杂性，难以通过设计一定的规则实现模型自动分类。因此，一些学者采用机器学习技术解决三维CAD模型的自动分类问题。受传统机器学习技术的制约，已有方法效果并不理想，无法应用于实际工业生产。近年来，深度学习取得了突破性进展，在图像分类、自然语言处理、语音识别等领域获得了不错效果。本文探索利用机器学习领域的最新研究成果——深度学习技术，对工程领域中常见的三维CAD模型进行自动分类。
创新要点：利用深度学习技术，模拟工程师人工识别三维零件的三个主要环节，构建了CAD模型的自动分类器；采用多种行之有效的训练策略，得到具有较好泛化性能的分类器。
研究方法：基于深度学习的三维CAD模型分类方法的主要步骤为：从制造企业获取足够的样本数据，构建三维CAD模型数据集；分析CAD领域的特点，选取合适的三维模型描述符进行特征提取，并处理为输入向量；利用深度学习技术模拟工程师人工识别工程零件的过程，设计分类器的拓扑结构并选择相应的组成元素，构建深层神经网络分类器；采用多种实用的训练技巧，使训练得到的分类器的自由参数取值尽可能逼近全局最优解，避免过拟合。
重要结论：实验数据表明，将深度学习技术与CAD领域知识结合，能够取得满意效果。深层神经网络分类器在测试集上的分类误差率低，足以满足工业生产的需求。
关键词：
CAD模型分类,
设计重用,
机器学习,
神经网络
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