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

3D模型在许多不同的领域有着广泛的应用，如工程、生物、化学、医学、娱乐和文化遗产。许多3D模型可从因特网和其它来源获得，从而导致如何有效且高效地找到所需3D模型（即，3D模型检索）。目前三维模型检索的研究主要集中在全局刚性三维模型的检索上，而对于非刚性和局部三维模型的检索，解决这一问题的算法并不有效。由于许多感兴趣的3D模型是非刚性的（例如人类和机构），并且由于仅考虑3D模型的部分（例如找到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模型是一个紧迫而具有挑战性的问题。基于形状的三维模型检索算法包括模型预处理、特征/形状描述子提取、特征/形状索引与匹配三个主要步骤。本项目将研究所有这三个步骤，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。将控制理论中的集员估计引入模型预处理和特征/形状描述子提取。新的机器学习方法，如亲和传播，流形学习和排名，将探索提取特征/形状描述符，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。本计划将研究其他来自影像处理及计算机视觉的新技术，探讨其在非刚性及部分3D模型检索方面的有效性，并探讨新技术的潜在应用。3D模型检索算法将与Delcam plc联合进行评估，以便进行商业开发。一个实用的非刚性和部分三维模型搜索引擎将被开发和部署在互联网上供公众使用。

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.