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 multi-annotator Gaussian Process Regression and establish a parallel with the standard probabilistic registration. The achieved method 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 pre-existing shape models. Experiments are conducted both for a 2D small dataset with diverse transformations and a 3D dataset of ears.

翻译：我们提出了一种基于高斯过程形式的形状拟合/配准方法，适用于存在大范围缺失数据的形状。高斯过程已被证明是一种强大的工具，因为它为形状建模和拟合提供了统一的框架。虽然现有方法在人类头部的常规情况下表现良好，但在处理更精细、变形更严重且缺失数据发生率较高的数据（例如耳朵）时，结果并不令人满意。为克服这一问题，我们将形状拟合问题表述为多标注者高斯过程回归，并与标准概率配准建立并行关系。所提出的方法SFGP在处理大范围缺失数据时，与当前最先进的配准方法及基于现有形状模型的配准方法相比，展现出更优的性能。实验分别在具有多种变换的二维小数据集和三维耳朵数据集上进行。