Probabilistic registration for Gaussian Process 3D shape modelling in the presence of extensive missing data

Abstract

We propose a shape fitting/registration method based on a Gaussian processes formulation, suitable for shapes with extensive regions of missing data. Gaussian processes are a proven powerful tool, as they provide a unified setting for shape modelling and fitting. While the existing methods in this area prove to work well for the general case of the human head, when looking at more detailed and deformed data, with a high prevalence of missing data, such as the ears, the results are not satisfactory. In order to overcome this, we formulate the shape fitting problem as a multiannotator Gaussian process regression and establish a parallel with the standard probabilistic registration. The achieved method, the shape fitting Gaussian process (or SFGP), shows better performance when dealing with extensive areas of missing data when compared to a state-of-the-art registration method and current approaches for registration with GP. Experiments are conducted both for a two-dimensional small dataset with several transformations and a three-dimensional dataset of ears.