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

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 geometric models are widely used in many different areas, such as engineering, chemistry, biology, medicine, cultural heritage, and entertainment. The broad availability of 3D models from the Internet and other sources has stimulated interest in the development of effective 3D model search/retrieval techniques. This project aims to develop robust and sensitive algorithms for non-rigid and partial 3D model retrieval. It will be of benefit to both academia and business. In addition, it is also expected to be beneficial to the advance in medical science and cultural heritage protection.

In engineering, computer-aided design (CAD) uses digital models of 3D objects for design. When many models are available, it is inefficient to manually search for required 3D models. This project will develop effective 3D model retrieval algorithms which can help CAD designers to find CAD models quickly. The algorithms can also be combined into existing CAD software packages to enhance their functionality. The project is potentially beneficial to both CAD end users and CAD software developers. We will cooperate with Delcam, one of the world's leading CAD software suppliers, to explore potential applications of our algorithms in their CAD software.

In chemistry and molecular biology, molecules and proteins are modelled as 3D objects, for structural classification and drug design, etc. Currently, structure classification is largely manual work. Given that protein databases are usually large, automatic classification using protein geometric features becomes very important. In classifying different proteins, there is a need for deformable (non-rigid) or partial shape matching. The local feature extraction and matching methods developed in this project are thus potentially usable for protein structure classification. In drug design, it is necessary to search for and compare deformable 3D models within molecular reference databases, so that suitable drug molecular structures can be designed to cure specific diseases. The 3D model search engine developed in this project has the potential to be used for this purpose.

In medical imaging applications, 3D volume data obtained by CT and MRI scans are currently used for diagnosis of organ deformations by matching actual images with medical databases of known deformations. Non-rigid 3D model retrieval algorithms are required since deformations are the prominent feature in these applications.

Many cultural heritage objects, such as old buildings, statues, pottery, and ceramics are digitized as 3D models in order to preserve cultural heritage, and also to make it available to a wider public. 3D model matching can be used to find similar artefacts for research purposes, or to compare classes of objects. For example, given pots from an archaeological site, they can be compared to known examples in a database as a basis for determing their the age or origin.

One of the most important application areas of non-rigid and partial 3D model retrieval is the field of entertainment. Animations in films or computer games need many non-rigid or part models of 3D characters for rendering. These models can be obtained from the Internet and 3D model manufacturers. However, 3D model databases are usually very large, and it is not easy to find specific 3D models from large databases. Our 3D search engines have the potential to save time for entertainment media production.

Besides the above-mentioned applications, 3D model retrieval also has potential applications in virtual geography environments (VGE), 3D spatial terrain, and robotics.