Rigid and Articulated Point Registration with Expectation Conditional Maximization
This work provides an incremental improvement in robust point registration for computer vision and robotics applications, particularly for handling complex shape matching with outliers.
This paper tackles the problem of matching rigid and articulated shapes using probabilistic point registration. The authors introduce the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm, which allows for general covariance matrices and improves upon isotropic covariance cases. The method also extends to articulated registration and incorporates outlier rejection.
This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration. The problem is recast into a missing data framework where unknown correspondences are handled via mixture models. Adopting a maximum likelihood principle, we introduce an innovative EM-like algorithm, namely the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm. The algorithm allows the use of general covariance matrices for the mixture model components and improves over the isotropic covariance case. We analyse in detail the associated consequences in terms of estimation of the registration parameters, and we propose an optimal method for estimating the rotational and translational parameters based on semi-definite positive relaxation. We extend rigid registration to articulated registration. Robustness is ensured by detecting and rejecting outliers through the addition of a uniform component to the Gaussian mixture model at hand. We provide an in-depth analysis of our method and we compare it both theoretically and experimentally with other robust methods for point registration.