Dependent landmark drift: robust point set registration with a Gaussian mixture model and a statistical shape model

TL;DR

This paper introduces a robust point set registration method that incorporates prior shape knowledge via a Gaussian mixture model and statistical shape model, improving accuracy especially with outliers, missing data, or rotations.

Contribution

It presents a novel registration approach combining Gaussian mixture models with statistical shape priors, enhancing robustness and scalability over existing methods.

Findings

Effective with outliers and missing data

Handles rotations and large point sets efficiently

Outperforms existing algorithms in shape datasets

Abstract

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.