基于深度卷积神经网络的三维模型检索

doi:10.11896／j.issn.1002-137X.2019.01.043

摘要/Abstract

摘要： 为了更有效地利用三维模型数据集进行特征的自主学习,提出一种使用自然图像作为输入源,以三维模型的较优视图集为基础,通过深度卷积神经网络的训练提取深度特征用于检索的三维模型检索方法。首先,从多个视点对三维模型进行视图提取,并根据灰度熵的排序选取较优视图;然后,通过深度卷积神经网络对视图集进行训练,从而提取较优视图的深度特征并进行降维,同时,对输入的自然图像提取边缘轮廓图,经过相似度匹配获得一组三维模型;最后,基于检索结果中同类模型总数占检索列表长度的比例对列表进行重排序,从而获得最终的检索结果。实验结果表明,该算法能够有效利用深度卷积神经网络对三维模型的视图进行深度特征提取,同时降低了输入源的获取难度,有效提高了检索效果。

关键词: 三维模型检索, 深度学习, 视图选取, 特征提取

Abstract: In order to make use of 3D model data set for feature self-learning more effectrively,this paper proposed a 3D model retrieval method,in which the natural images are as input sources,a better view set of 3D model is as basis,and the depth features obtained through training deep convolutional neural networks are applied for retrieval.Firstly,the 3D model views are extracted from multiple viewpoints and the optimal view is selected according to the order of gray entropy.Secondly,the view set is trained through deep convolution neural network,the depth features of the optimal view are extracted and its dimension is reduced.At the same time,edge contouring is extracted for the input natural image and a set of 3D models is gotten after similarity matching.Finally,according to the ratio of the total number of similar models to the length of the search list in the retrieval result,the retrieval list is reordered and the final result will be gained.Experimental results show that the algorithm can effectively use depth convolution neural network to extract the depth feature of the view of the 3D model,meanwhile reduce the difficulty of obtaining the input source,and improve the retrieval efficiency effectively.

Key words: 3D model retrieval, Deep learning, Feature extraction, View selection

中图分类号:

TP391

刘志, 李江川. 基于深度卷积神经网络的三维模型检索[J]. 计算机科学, 2019, 46(1): 278-284. https://doi.org/10.11896／j.issn.1002-137X.2019.01.043

LIU Zhi, LI Jiang-chuan. 3D Model Retrieval Based on Deep Convolution Neural Network[J]. Computer Science, 2019, 46(1): 278-284. https://doi.org/10.11896／j.issn.1002-137X.2019.01.043

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