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基于深度体素卷积神经网络的三维模型识别分类

Recognition and Classification for Three-Dimensional Model Based on Deep Voxel Convolution Neural Network

杨军   王顺   周鹏  
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摘要

提出一种基于深度体素卷积神经网络的三维(3D)模型识别分类算法, 该算法使用体素化技术将3D多边形网格模型转化为体素矩阵, 并通过深度体素卷积神经网络提取该矩阵的深层特征, 以增强特征的表达能力和差异性。在ModelNet40数据集上的实验结果表明:所提算法对3D网格模型识别分类的准确率能够达到87%左右。所构建的深度体素卷积神经网络能够有效地增强3D模型的特征提取和表达能力, 提高对大规模复杂3D网格模型分类识别的准确率, 所提方法优于当前的主流方法。

Abstract

An algorithm of recognition and classification of three-dimensional (3D) model based on deep voxel convolution neural network is proposed. The voxelization technology is used to transform 3D polygon mesh model into a voxel matrix, and the deep features of the matrix are extracted by the deep voxel convolution neural network to enhance the expressive ability and difference of the features. The experimental results on ModelNet40 dataset show that the accuracy of the algorithm can reach about 87% for recognizing and classifying 3D mesh model. The constructed deep voxel convolution neural network can effectively enhance the feature extraction and expression ability of 3D model, as well as improve the classification accuracy of large-scale complex 3D mesh models, which is better than the current mainstream methods.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.4

DOI:10.3788/aos201939.0415007

所属栏目:机器视觉

基金项目:国家自然科学基金(61862039, 61462059)

收稿日期:2018-10-26

修改稿日期:2018-12-11

网络出版日期:--

作者单位    点击查看

杨军:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070
王顺:兰州交通大学自动化与电气工程学院, 甘肃 兰州 730070
周鹏:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070

联系人作者:杨军(yangj@mail.lzjtu.cn)

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引用该论文

Yang Jun,Wang Shun,Zhou Peng. Recognition and Classification for Three-Dimensional Model Based on Deep Voxel Convolution Neural Network[J]. Acta Optica Sinica, 2019, 39(4): 0415007

杨军,王顺,周鹏. 基于深度体素卷积神经网络的三维模型识别分类[J]. 光学学报, 2019, 39(4): 0415007

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