A New Framework for Fitting Shape Models to Range Scans: Local Statistical Shape Priors Without Correspondences

Abstract

Statistical shape models provide an important means in many applications in computer vision and computer graphics. However, the major problems are that the majority of these shape models require dense pointcorrespondences along all training shapes and that a large number of training shapes is needed in order to capture the full amount of intra-class shape variation. In this contribution, we focus on a statistical shape model that can be constructed from a set of training shapes without defining any point-correspondences. Additionally, we show how a local statistical shape model can make better use of the available shape information, greatly reducing the number of required training shapes. Finally, we present a new framework to fit this local statistical shape model without correspondences to range scans that represent incomplete parts of the trained shape class. The fitted model is then used to reproduce a natural-looking approximation of the complete shape.