In order to enable photogrammetric tracking of objects from a computed tomography (CT) dataset with a multi-camera system, a transformation between the CT data space and a photogrammetric reference frame is required, typically based on control points. To achieve a robust and precise registration between CT and photogrammetric datasets, this work proposes a marker-based approach. The main goal is to use a marker model that allows straightforward segmentation and control point estimation in CT voxel space, while also supporting reliable and precise control point estimation in the photogrammetric images. As a proof-of-concept, spherical markers were investigated, since they allow centre estimation in both domains. In the CT data, marker centres were determined by intensity-based thresholding followed by sphere fitting, while in the photogrammetric data they were estimated by intensity-based thresholding, edge detection, circle fitting, and multi-image spatial intersection. Two different marker models were tested. The results show that the proposed method is feasible and yields sub-millimetre standard deviations of unit weight for both marker types. However, since a sufficient stochastic model is not yet available, the reported accuracy measures may be optimistic and should therefore be interpreted with caution. Future work will address these limitations, in particular uncertainty modelling as well as remaining lighting and contrast issues.