课题基金 / 基金详情

Robust and Sensitive Methods for Non-rigid and Partial 3D model Retrieval

Robust and Sensitive Methods for Non-rigid and Partial 3D model Retrieval
用于非刚性和部分 3D 模型检索的稳健且灵敏的方法
批准号:
EP/J02211X/1
负责人:
Xianfang Sun
金额:
$39.49万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2013
资助国家:
英国
项目状态:
已结题
起止时间:
2013 至 --

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中文摘要
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英文摘要
3D models have a broad range of applications in many different areas such as engineering, biology, chemistry, medicine, entertainment and cultural heritage. Many 3D models are available from the Internet and other sources, resulting in a problem of how to effectively and efficiently find required 3D models (i.e., 3D model retrieval). Current research on 3D model retrieval mainly focuses on global rigid 3D model retrieval, and algorithms for solving this problem are not effective for non-rigid and partial 3D model retrieval. Because many 3D models of interest are non-rigid (such as humans, and mechanisms), and because it is often important to consider just parts of a 3D model (e.g. find a model with a particular connector), finding an efficient way to retrieve non-rigid and partial 3D models is a pressing and challenging problem. This project intends to develop robust and sensitive algorithms for non-rigid and partial 3D model retrieval.A typical shape-based 3D model retrieval algorithm consists of three main steps: model preprocessing, feature/shape descriptor extraction, and feature/shape indexing and matching. This project will investigate all three steps and develop new non-rigid and partial 3D model retrieval algorithms based on novel techniques from other research areas. Set-membership estimation from control theory will be introduced into model preprocessing and feature/shape descriptor extraction. New machine learning methods, such as affinity propagation, manifold learning and ranking, will be explored for extracting features/shape descriptors, and for feature/shape indexing and matching. The N-gram model from natural language processing will be adapted to feature/shape indexing and matching. Other new techniques from image processing and computer vision will be investigated regarding their effectiveness for non-rigid and partial 3D model retrieval.This project will also consider potential applications of the newly developed techniques. The 3D model retrieval algorithms will be evaluated jointly with Delcam plc with a view to commercial exploitation. A practical non-rigid and partial 3D model search engine will be developed and deployed on the Internet for public use.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1007/s41095-016-0045-5
发表时间: 2016-04
期刊: Computational Visual Media
影响因子: 6.9
作者: [D. Pickup;Xianfang Sun;Paul L. Rosin;Ralph Robert Martin]
通讯作者: D. Pickup;Xianfang Sun;Paul L. Rosin;Ralph Robert Martin
DOI: 10.1016/j.gmod.2021.101099
发表时间: 2021
期刊: Graph. Model.
影响因子: --
作者: [Zhongping Ji;Xianfang Sun;Yu-Wei Zhang;Weiyin Ma;Mingqiang Wei]
通讯作者: Zhongping Ji;Xianfang Sun;Yu-Wei Zhang;Weiyin Ma;Mingqiang Wei
Canonical Forms for Non-Rigid 3D Shape Retrieval
非刚性 3D 形状检索的规范形式
DOI: 10.2312/3dor.20151063
发表时间: 2015
期刊:
影响因子: --
作者: [Pickup D]
通讯作者: Pickup D
DOI: 10.2312/3dor.20151064
发表时间: 2015-05
期刊:
影响因子: --
作者: [Z. Lian;J. Zhang;S. Choi;H. ElNaghy;Jihad El-Sana;T. Furuya;Andrea Giachetti;R. Güler;L. Lai]
通讯作者: Z. Lian;J. Zhang;S. Choi;H. ElNaghy;Jihad El-Sana;T. Furuya;Andrea Giachetti;R. Güler;L. Lai
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    海外基金