面向三维模型检索的主动式复结构图学习方法研究

项目名称： 面向三维模型检索的主动式复结构图学习方法研究

项目编号： No.61272297

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 赵向军

作者单位： 江苏师范大学

项目金额： 80万元

中文摘要： 三维模型局部检索方法因其强大的分析能力和区别能力，成为模型重用的重要工具。本项目包括两大部分，第一部分研究方向无关、形变无关的特征匹配方法，该方法在局部检索中不可缺少。不同于传统的曲面SIFT，本研究将三维模型嵌入到体网格中，通过SIFT的三维推广，提出了完整的匹配方法。借助稀疏表达进行模型简化，并对简化模型进行自适应体素化以降低时空消耗，基于流形学习完成形变无关匹配。第二部分面向三维模型检索提出一新型机器学习方法。借助特征匹配完成语义流形构造，进而提出复结构图学习方法，作为多语义检索的统一表示模型。通过特征匹配和组件分析获取语义知识，借助领域知识构建标记缺失区域检测算法，从而获得主动图学习方法。本项目着眼于机器学习与三维模型检索的深度交叉，借助机器学习解决模型检索问题，在解决问题的同时，又丰富了机器学习理论，纵观信息检索相关领域可知，本研究顺应了学科发展趋势。

中文关键词： 三维模型检索；特征表示；机器学习；进化算法；

英文摘要： 3D model partial retrieval has been the indispensable tool in model reuse, thanks to its strong ability of analysis and discrimination. This project is focused on partial retrieval techniques, including the following two aspects. The first one is a novel approach for robust feature matching which is essential for 3D model partial retrieval. Firstly, 3D model with boundary representation(B-rep) is embed in a voxel grid, and a novel feature matching approach is proposed by extending SIFT from 2D to 3D. Secondly, A model simplification algorithm is proposed based on sparse representation, and then an adaptive method is used for voxelization of B-rep model so as to reduce the time and memory costs. Thirdly, with the help of manifold learning, feature vector matching is deformation invariant. In the second part, we present a new machine learning method for 3D model retrieval, i.e. active 2-tier graph learning. Firstly, based on manifold learning, the similarity between two models is better measured at the semantic level. Secondly the similarity between 3D models is analyzed with respect to shape and semantics. Naturally a 2-tier graph learning method is introduced as the general representative model. Thirdly, according to the semantic information obtained by structural analysis, the node with absence of lab

英文关键词： Machin learning；Feature presentation；Machine learning；Evolutionary algorithm；