Rigid Point Registration with Expectation Conditional Maximization

TL;DR

This paper introduces an ECMPR method for rigid 3D-2D point registration, comparing two algorithms for estimating rotation and translation, with theoretical and experimental analysis of their performance.

Contribution

It presents a novel ECMPR approach for rigid point registration and compares two optimization algorithms within this framework.

Findings

ECMPR effectively estimates registration parameters.

Two algorithms show different convergence behaviors.

Theoretical analysis supports experimental results.

Abstract

This paper addresses the issue of matching rigid 3D object points with 2D image points through point registration based on maximum likelihood principle in computer simulated images. Perspective projection is necessary when transforming 3D coordinate into 2D. The problem then recasts into a missing data framework where unknown correspondences are handled via mixture models. Adopting the Expectation Conditional Maximization for Point Registration (ECMPR), two different rotation and translation optimization algorithms are compared in this paper. We analyze in detail the associated consequences in terms of estimation of the registration parameters theoretically and experimentally.