Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces

TL;DR

This paper reviews and compares various statistical shape models for 3D data, focusing on human faces, analyzing their theoretical properties and practical performance in noisy and occluded conditions.

Contribution

It provides a comprehensive analysis and comparison of global and local statistical shape models for 3D data, with extensive experiments on human face datasets.

Findings

Global models excel in capturing overall shape variation.

Local models better handle fine details and local variations.

The choice of model depends on specific application requirements.

Abstract

With systems for acquiring 3D surface data being evermore commonplace, it has become important to reliably extract specific shapes from the acquired data. In the presence of noise and occlusions, this can be done through the use of statistical shape models, which are learned from databases of clean examples of the shape in question. In this paper, we review, analyze and compare different statistical models: from those that analyze the variation in geometry globally to those that analyze the variation in geometry locally. We first review how different types of models have been used in the literature, then proceed to define the models and analyze them theoretically, in terms of both their statistical and computational aspects. We then perform extensive experimental comparison on the task of model fitting, and give intuition about which type of model is better for a few applications. Due to the wide availability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.