A previous publication has described a method of pairwise 3D surface correspondence for the automated generation of landmarks on a set of examples from a class of shape. In this paper we describe a set of improved algorithms which give more accurate and more robust results. We show how the pairwise corresponder can be used in a extension of an existing framework for establishing dense correspondences between a set of training examples to build a 3D Point Distribution Model. Examples are given for both synthetic and real data.
Keywords:
Correspondence, Triangle Decimation, Automatic Landmarks, Point Distribution Models, Three Dimensional, 3D, ICP
