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Details of Grant 

EPSRC Reference: EP/J02211X/1
Title: Robust and Sensitive Methods for Non-rigid and Partial 3D model Retrieval
Principal Investigator: Sun, Dr X
Other Investigators:
Martin, Professor RR Rosin, Professor P
Researcher Co-Investigators:
Project Partners:
Delcam International plc
Department: Computer Science
Organisation: Cardiff University
Scheme: Standard Research
Starts: 22 April 2013 Ends: 21 August 2016 Value (£): 309,947
EPSRC Research Topic Classifications:
Computer Graphics & Visual. Image & Vision Computing
EPSRC Industrial Sector Classifications:
Creative Industries
Related Grants:
Panel History:
Panel DatePanel NameOutcome
18 Jul 2012 EPSRC ICT Responsive Mode - July 2012 Announced
Summary on Grant Application Form
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.
Key Findings
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