I am in the 4th year of study for my PhD in the School of Computer Science & Informatics. My research focuses primarily on the study of 3D geometry and shape correspondence.
For more information please visit my personal website.
High Quality 3D Geometry and Appearance Reconstruction of Non-Rigidly Deforming Objects using Low-Cost RGB-D Cameras
Capturing and reconstruction of high quality 3D geometry and the appearance of non-rigidly deforming objects, such as the dynamics of human actions, is essential for many applications, including movie and game production in the creative industries, Virtual Reality (VR) videoconferencing, analysing human behaviour for healthcare monitoring and sports analysis etc. Despite great effort, it is still a challenging problem, especially when large-scale deformations are involved: self-occlusion, subtle geometry and appearance change (e.g. wrinkles of skin and clothing) all contribute to the difficulty.
This research aims to advance the state of the art by investigating novel data-driven techniques to address fundamental challenges. Low-cost RGB-Depth cameras have become more capable in recent years and will be used in the research to make the techniques widely useful. We will develop a new joint representation and analysis technique for both geometry and appearance, to effectively encode geometric and appearance change during non-rigid deformation. The plausible deformation and change in appearance typically form a low dimensional manifold embedded in this joint space. To address the issues of noise and incompleteness in the scanned data, machine learning techniques such as manifold learning will be exploited. This will effectively utilise information from any previous scans to fill the gaps and improve the quality of reconstruction. An optimisation framework will also be developed incorporating knowledge from the manifold as well as sparse priors.