基于深度学习的三维模型检索研究  

Research on 3D model retrieval based on deep learning

作　　者：张静 曲志坚[1] 刘晓红[1] ZHANG Jing;QU Zhijian;LIU Xiaohong(College of Computer Science and Technology,Shandong University of Technology,Zibo Shandong 255049,China)

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255049

出　　处：《智能计算机与应用》2019年第3期54-58,共5页Intelligent Computer and Applications

摘　　要：基于卷积神经网络在图像领域的发展,本文研究了将卷积神经网络应用到三维模型检索领域。对三维模型预处理后,选择6个投影角度把模型投影成6幅二维图像,利用提取的视图作为神经网络的输入,利用深度学习框架提取图像特征作为最终的模型描述符。之后通过比较2个模型多个视角的二维投影的相似度,如果两者间相似,则三维模型也是相似的,再取多维视图的相似度平均值得到2个三维模型的最终相似度,选择最终相似度最大的10个模型作为结果输出。充分利用二维图像领域性能优越的网络架构,并且存在海量图像数据供深度学习模型进行预训练。从检索结果看出,该方法能够得到较好的效果。Based on the development of Convolution Neural Network in the field of image,this paper studies the application of Convolution Neural Network to 3D model retrieval.After pre-processing the three-dimensional model,six projection angles are selected to project the model into six two-dimensional images.The extracted views are used as the input of neural network,and the image features are extracted by depth learning framework as the final model descriptors.Then the paper compares the similarity of the two models' two-dimensional projections from multiple perspectives.If the corresponding models are similar,then the three-dimensional models are similar.The paper takes the average similarity of the multi-dimensional views to get the final similarity of the two three-dimensional models,and select 10 models with the maximum similarity as the output of the results.It makes full use of the network architecture with superior performance in the field of two-dimensional images,and there is a huge amount of image data for depth learning model pre-training.From the retrieval results,it is demonstrated that this method can get better retrieval.

关 键 词：卷积神经网络 视图 特征提取 三维模型检索 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]