Gaussian Processes for Shape Modelling: a Probabilistic Registration Approach
Shape models find increasing applications in fields ranging from medicine to security or animation. Gaussian Processes (GP) have emerged as a powerful framework for developing 3D shape models and performing related tasks in a unified manner. However, existing methods rely on the availability of a large and complete dataset, which can be costly and time-consuming to acquire, often requiring extensive manual input and application-specific pre-processing methods. A more common setting is a dataset with large regions of missing data and outliers, where standard techniques have limited performance. I will discuss a new registration/fitting method derived within the GP framework, which is designed to overcome these challenges. By drawing a parallel with state-of-the-art probabilistic registration algorithms, the proposed method provides a more principled approach and achieves better results in a realistic dataset.
Filipa Valdeira is a researcher at NOVA School of Science and Technology (Portugal). She holds a Ph.D. in Mathematical Sciences from the University of Milan (Italy), and a B.Sc. and M.Sc. in Aerospace Engineering from Instituto Superior Técnico (Portugal). Her Ph.D. research was focused on the application of optimization, statistics, and probability for the development of statistical shape models, with application to an industrial setting.