Dependent landmark drift: robust point set registration with a Gaussian mixture model and a statistical shape model
This work addresses robust point set registration for applications like human body, hand, and face modeling, representing an incremental improvement by integrating existing statistical shape models with transformation techniques.
The paper tackles point set registration by incorporating prior shape information into a Gaussian mixture model, resulting in a method that effectively handles outliers, missing regions, and rotations while reducing computational cost to linear scaling.
The goal of point set registration is to find point-by-point correspondences between point sets, each of which characterizes the shape of an object. Because local preservation of object geometry is assumed, prevalent algorithms in the area can often elegantly solve the problems without using geometric information specific to the objects. This means that registration performance can be further improved by using prior knowledge of object geometry. In this paper, we propose a novel point set registration method using the Gaussian mixture model with prior shape information encoded as a statistical shape model. Our transformation model is defined as a combination of the similar transformation, motion coherence, and the statistical shape model. Therefore, the proposed method works effectively if the target point set includes outliers and missing regions, or if it is rotated. The computational cost can be reduced to linear, and therefore the method is scalable to large point sets. The effectiveness of the method will be verified through comparisons with existing algorithms using datasets concerning human body shapes, hands, and faces.