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 intri...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.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.61163016 and 61173125)
文摘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.
