Intensity-based 2D-3D medical image registration is a special case of the pose estimation problem from computer vision with many applications in medicine. This work presents an overview of the 2D-3D intensity-based image registration problem in the medical domain as well as results from several methods developed to aid in its practice. In particular: 1) Light field rendering techniques from the graphics community are extended to rapidly generate digitally reconstructed radiographs (DRRs). 2) A full 2D-3D registration algorithm using light field DRRs is presented and validated against a real, clinical gold standard. 3) A new, hybrid similarity measure is presented that is a weighted combination of an intensity-based image similarity measure and a point-based measure incorporating a single fiducial marker. 4) Finally, a novel similarity measure called regional mutual information (RMI) is introduced. RMI is an extension of mutual information which incorporates spatial information in a principled way. The additional spatial information helps make its use as a similarity measure much more robust to initial misregistration than standard mutual information.