Applications are invited for a PhD Studentship under the supervision of Lourdes Agapito to undertake research on the topic of "Dense Non-Rigid and Articulated 3D Reconstruction from Monocular Video". This PhD project aims to provide optimization methods for detailed life-like 3D reconstructions of non-rigid objects from video taken with a single camera.
The successful candidate will develop models and algorithms for the registration, tracking and 3D reconstruction of deformable and articulated objects such as a human face, or a cheetah on the hunt. The focus of the project will be on challenging real out-of-the-lab applications such as laparoscopic surgery, 3D graphics animation or reconstruction of sports players.
This PhD position will allow the candidate to join a strong research team, which is leading state of the art research into non-rigid 3D reconstruction from motion. Building on our current reconstruction and registration techniques, the aim is to provide life-like 3D models directly from raw video footage.
Over the course of the PhD, the successful candidate will refine and derive novel models and optimization techniques, and a high level of mathematical sophistication is expected. Preference will be shown to candidates with experience in discrete optimization (particularly graph-cuts), or continuous optimization and factorization techniques.
The PhD will be based at the Queen Mary Vision Laboratory in the School of Electronic Engineering and Computer Science, Queen Mary University of London. Queen Mary Vision Laboratory is one of the leading computer vision research laboratories in the UK. Dr Agapito's group, largely funded by an ERC grant, specialises in non-rigid structure from motion, 3D reconstruction and tracking of deformable and articulated objects, human motion analysis and motion segmentation.
The candidate should have a first or upper second honours degree or equivalent in Computer Science, Electronic Engineering, Mathematics, Physics, or a related field, and be able to demonstrate strong mathematical and analytical skills. Excellent programming skills are required, preferably with Matlab or C++. Research experience in image processing, computer vision, or machine learning is desirable.
The studentship will cover student fees and a tax-free stipend starting at £15,590 per annum and is available to candidates of all nationalities. Informal enquires can be made by email to Dr. Lourdes Agapito.
To apply please email the following documents to Dr. Lourdes Agapito: a completed application form, a CV listing all publications, your representative publications in PDF format, 3 reference letters, a research statement and other relevant documents as requested (see www.qmul.ac.uk/postgraduate/apply/). These documents must also be submitted online following the instructions given in the link.