Accurate and precise 2D3D registration based on X-ray intensity
This paper addresses the problem of estimating the 3D rigid pose of an object from its digitized X-ray projection. We considered the cases of homogeneous (CAD models) and inhomogeneous (attenuation map obtained from computed tomography) X-ray attenuation in an optimization framework based on a mutual information similarity measure. Convergence of object pose recovery is highly precise and obtained with sub-millimeter accuracy for both screen-film and digital radiographs by three major enhancements: (i) special care is given to the model of Parzen distribution used in the mutual information estimator (data pre-sphering in the bivariate case and bandwidth estimation in the univariate case); (ii) a quasi-global optimization scheme based on a modified version of stochastic clustering is used in conjunction with an object mesh resampling stage to reduce variance of the final pose estimator; (iii) nonlinear response to the radiograph is also estimated for screen-film radiographs.
2d3d registration computed tomography mutual information object pose stochastic clustering digital radiography