深度特征融合的非刚性三维形状局部匹配  

Non Rigid 3D Shape Partial Matching Based on Deep Feature Fusion

作　　者：韩丽[1] 佟宇宁 朴京钰 徐圣斯 王晓旻 兰鹏燕 于冰 Han Li;Tong Yuning;Piao Jingyu;Xu Shengsi;Wang Xiaomin;Lan Pengyan;Yu Bing(Faculty of Computer and Information Technology,Liaoning Normal University,Dalian 116000)

机构地区：[1]辽宁师范大学计算机与信息技术学院,大连116000

出　　处：《计算机辅助设计与图形学学报》2021年第3期475-486,共12页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61702246);辽宁省科技厅项目(2020JH4/10100045);辽宁省教育厅项目(603200052096);大连市科技创新基金(2019J12GX038).

摘　　要：针对海量、异构三维形状匹配与智能检索技术的需求,提出了一种基于级联卷积神经网络(F-PointCNN)深度特征融合的三维形状局部匹配方法.首先,采用特征袋模型,提出几何图像表示方法,该几何图像不仅能够有效区分同类异构的非刚性三维模型,而且能够揭示大尺度不完整三维模型的结构相似性.其次,构建级联卷积神经网络学习框架F-PointCNN,其中,BoF-CNN从几何图像中学习深度全局特征,建立融合局部特征与全局特征的点特征表示;进而对Point-CNN进行点特征的细化与提纯,生成具有丰富信息的深度融合特征,有效提高形状特征的区分性与鲁棒性.最终,通过交叉矩阵度量方法高效实现非刚性三维模型的局部形状匹配.在公开的非刚性三维模型数据库的实验结果表明,该方法提取的特征在大尺度变换的形状分类及局部形状匹配中具有更强的识别力与更高的匹配精度.To meet the requirements of massive and heterogeneous 3D shape partial matching and intelligent retrieval technology,a 3D shape local matching method based on the deep fusion feature of F-PointCNN is proposed.First,the feature bag(BoF)learning model is used to propose an geometric image representation,which can not only effectively distinguish heterogeneous non-rigid 3D models of the same kind,but also reveal the structural similarity of large-scale incomplete 3D models.Next,a cascaded convolutional neural network slearning framework(F-PointCNN)is constructed,where BoF-CNN learns the deep global feature from BoF geometric images and establishes the point feature representation that integrates the local feature and the global feature;Point-CNN refines the point feature and generates deep feature representation which effectively improves the discriminative ability and robustness.Finally,the local shape matching of non rigid 3D model is realized by cross matrix measurement.The open non-rigid 3D shape databases are used to carry out a series of experiments,the results show that the features extracted by proposed method have stronger discriminative ability in large-scale transformation shape classification and higher precision in partial shape matching.

关 键 词：局部匹配 特征融合 卷积神经网络 非刚性三维模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]