摘要
由于三维CAD模型内在的复杂性,实现模型的自动分类是一个难题。所以提出了一种基于卷积神经网络(Convolutional Neural Network,CNN)的三维CAD模型自动分类方法,首先采用球体将三维CAD模型完全包住,获取模型沿固定视角的二维投影视图集;然后采用Apriori甄选出其中的典型视图,将典型视图作为卷积神经网的输入;在AlexNet模型的基础上进行参数调整,并将其作为三维CAD模型分类器;最后选取正向传播和反向传播相结合的方式对卷积神经网络进行训练,以提高其泛化性能。实验表明,该方法能够提高模型分类的准确性和效率。
Due to the intrinsic complexity of 3D CAD models,the automatic model classification methods are scarce.In this paper,an automatic 3D CAD model classification approach based on Convolutional Neural Network(CNN)is proposed.At first,in order to obtain 2D views along the fixed angle,we adopt the sphere to wrap the 3D CAD model entirely,then the typical views are selected from the 2D views based on Apriori,and then preprocessed as input vectors for category recognition.Parameter adjustment based on AlexNet model,a novel CNN classifier for 3D CAD models is constructed.Finally,forward propagation and back propagation are selected to train the convolutional neural network to improve its generalization performance.Experiments show that this method can improve the accuracy and efficiency of model classification.
作者
丁博
伊明
DING Bo;YI Ming(School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China)
出处
《哈尔滨理工大学学报》
CAS
北大核心
2020年第1期66-72,共7页
Journal of Harbin University of Science and Technology
基金
国家自然科学基金(61673142)
黑龙江省普通本科高校青年创新人才培养项目(UNPYSCT-2016034)。
