Scale-invariant registration of monocular stereo images to 3D surface models

We present an approach for scale recovery from monocular stereo images of an endoscopic camera with simultaneous registration to dense 3D surface models. We assume the camera motion to be unknown or at least uncertain. An example application is the registration of endoscope images to pre-operative CT scans that allows instrument navigation during surgical procedures. The application field is not restricted to the medical field. It can be extended to registration of monocular video images to laser-based surface reconstructions in, e.g., mobile navigation area or to autonomous aircraft navigation from topological surveys. A novel way for depth estimation from arbitrary camera motion is presented. In this paper, we focus on the robust initialization of the system and on the scale recovery for the reconstructed 3D point clouds with accurate registration to the candidate surfaces extracted from the CT data. We provide experimental validation of the algorithm with data obtained from our experiments with a phantom skull.