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: CARDIFF UNIVERSITY
• 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
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ02211X%2F1
• 关键词: Robust; Sensitive; Methods; Non; rigid

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.

3D模型在工程、生物、化学、医学、娱乐和文化遗产等许多不同领域有着广泛的应用。许多3D模型可以从互联网和其他来源获得，导致如何有效且高效地找到所需的3D模型（即3D模型检索）的问题。目前3D模型检索的研究主要集中在全局刚性3D模型检索上，解决该问题的算法对于非刚性和部分3D模型检索并不有效。由于许多感兴趣的 3D 模型都是非刚性的（例如人体和机构），并且由于仅考虑 3D 模型的一部分（例如查找具有特定连接器的模型）通常很重要，因此找到一种有效的方法来检索非刚性和部分 3D 模型是一个紧迫且具有挑战性的问题。该项目旨在开发用于非刚性和部分 3D 模型检索的稳健且灵敏的算法。典型的基于形状的 3D 模型检索算法由三个主要步骤组成：模型预处理、特征/形状描述符提取以及特征/形状索引和匹配。该项目将研究所有三个步骤，并基于其他研究领域的新技术开发新的非刚性和部分 3D 模型检索算法。来自控制理论的集合成员估计将被引入模型预处理和特征/形状描述符提取中。将探索新的机器学习方法，例如亲和力传播、流形学习和排序，以提取特征/形状描述符以及特征/形状索引和匹配。来自自然语言处理的 N-gram 模型将适用于特征/形状索引和匹配。将研究图像处理和计算机视觉的其他新技术在非刚性和部分 3D 模型检索方面的有效性。该项目还将考虑新开发技术的潜在应用。 3D 模型检索算法将与 Delcam plc 联合评估，以期进行商业开发。将开发实用的非刚性和部分3D模型搜索引擎并将其部署在互联网上供公众使用。

项目成果

Skeleton-based canonical forms for non-rigid 3D shape retrieval

• 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

Normal manipulation for bas-relief modeling

• DOI: 10.1016/j.gmod.2021.101099
• 发表时间: 2021
• 期刊: Graph. Model.
• 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
• 作者: Pickup D

Non-rigid 3D Shape Retrieval

• DOI: 10.2312/3dor.20151064
• 发表时间: 2015-05
• 作者: Z. Lian;J. Zhang;S. Choi;H. ElNaghy;Jihad El-Sana;T. Furuya;Andrea Giachetti;R. Güler;L. Lai

ReliefNet: Fast Bas-relief Generation from 3D Scenes

• DOI: 10.1016/j.cad.2020.102928
• 发表时间: 2021-01
• 期刊: Comput. Aided Des.
• 作者: Zhongping Ji;Wei Feng;Xianfang Sun;Fei-wei Qin;Yigang Wang;Yu-Wei Zhang;Weiyin Ma