Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (7): 1863-1872.DOI: 10.11772/j.issn.1001-9081.2019112034

• Artificial intelligence •     Next Articles

Overview of content and semantic based 3D model retrieval

PEI Yandong1, GU Kejiang2   

  1. 1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China;
    2. Sinopec Huadong Petroleum Oilfield Service Corporation, Nanjing Jiangsu 210019, China
  • Received:2019-11-04 Revised:2020-03-28 Online:2020-07-10 Published:2020-05-19

基于内容和语义的三维模型检索综述

裴焱栋1, 顾克江2   

  1. 1. 南京理工大学 计算机科学与工程学院, 南京 210094;
    2. 中石化华东石油工程有限公司, 南京 210019
  • 通讯作者: 顾克江
  • 作者简介:裴焱栋(1988-),男,江苏扬州人,博士研究生,CCF会员,主要研究方向:智能交通、Petri网;顾克江(1958-),男,江苏苏州人,教授级高级工程师,CCF高级会员,主要研究方向:模式识别、图像处理、计算机图形学。

Abstract: Retrieval of multimedia data is one of the most important issues in information reuse. As a key step of 3D modeling, 3D model retrieval has been deeply studied in recent years due to the widespread use of 3D modeling. Aiming at the current progress of 3D model retrieval technology, content-based retrieval technologies were firstly introduced. According to the extracted features, these technologies were divided into four categories:based on statistical data, based on geometric shape, based on topological structure and based on visual features. The main achievements, advantages and disadvantages of each technology were presented respectively. And then the semantic-based retrieval technologies considering semantic information to solve the "semantic gap" phenomenon were introduced. They were divided into three categories:relevance feedback, active learning and ontology technology. Then, the relationship and characteristics of these technologies were introduced. Finally, the future research directions of 3D model retrieval were concluded and proposed.

Key words: 3D model retrieval, shape matching, content, semantic

摘要: 多媒体信息的检索是信息复用的重要途径。三维模型检索作为三维建模过程中的关键技术之一,近年来随着三维建模的广泛运用而被深入研究。针对目前三维模型检索技术的进展,首先介绍了基于内容的检索技术,按照提取的特征将其分为四类:基于统计数据、基于几何外形、基于拓扑结构和基于视觉特征,分别介绍各类技术的主要成果和优缺点;然后介绍考虑语义信息,解决“语义鸿沟”现象的基于语义的检索方法,根据切入角度将其分为三类:相关性反馈、主动学习和本体技术,随后介绍了各类技术的相互关系与特点;最后总结和提出了三维模型检索的未来研究的发展方向。

关键词: 三维模型检索, 形状匹配, 内容, 语义

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