结构感知深度学习的三维形状分类方法  

3D Shape Classification Method Based on Shape-Aware Deep Learning

作　　者：韩丽[1] 朴京钰 兰鹏燕 王晓旻 于冰 佟宇宁 徐圣斯 Han Li;Piao Jingyu;Lan Pengyan;Wang Xiaomin;Yu Bing;Tong Yuning;Xu Shengsi(School of Computer Science and Information Technology,Liaoning Normal University,Dalian 116081)

机构地区：[1]辽宁师范大学计算机与信息技术学院,大连116081

出　　处：《计算机辅助设计与图形学学报》2021年第1期29-38,共10页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61702246);大连市科技创新基金(2019J12GX038)。

摘　　要：为了解决复杂、海量三维模型的形状识别问题,提出了一种结构感知深度学习的三维形状分类方法.通过联合学习三维模型的几何结构和空间结构,生成具有结构感知的特征向量表示,该特征向量具有更强的识别力与稳定性,在三维形状分类中取得显著的效果.首先,提取优化的多尺度热核特征,并通过CNN学习模型,有效地获取三维形状的几何结构特征;其次,建立多视图卷积学习网络提取三维形状的空间结构特征;最后,通过联合优化学习生成具有结构感知的深度特征表示.文中采用了C++,Matlab,TensorFlow框架实现,并在公开的三维数据库中进行了大量实验,实验结果表明,文中方法获取的深层结构特征对于复杂拓扑结构、大尺度几何形变的三维形状具有稳定性;与相关方法对比,在三维形状分类中具有更高的分类精度.In order to solve the problem of complex and massive 3D shapes recognition,we propose a structure-aware deep learning model for 3D shape classification.By learning the geometric structure and spatial structure jointly,we generate a structure-aware deep feature vector,which has stronger discriminative ability and stability on 3D shape classification.Firstly,optimal multi-scale HKS features are extracted to construct discerning geometric shape descriptor based on a CNN learning model.Secondly,a multi-view based CNN learning framework is built to extract spatial structure feature.Finally,all the features are jointly learned and generate a structure-aware feature vector.We explore our method by using C++,Matlab,TensorFlow platform,a serial of experimental analysis are carried out in the public 3D databases,they have shown that our deep features are stable for complex topological structure and large-scale geometric deformation.Compared with related methods,this method has higher classification accuracy in 3D shape classification.

关 键 词：形状分类 结构感知 卷积神经网络 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]