This paper describes the use of a probabilistic quality metric for planning camera placement for 3D reconstructions. A probabilistic quality metric estimates the probability of a reconstruction achieving a desired goal. This probabilistic model leads to the natural integration of many different factors influencing the quality of a reconstruction without relying on arbitrary weights for those factors. The specific factors addressed here are occlusions, feature matching, and feature localization. It is demonstrated how these factors impact the quality of a reconstruction and how they can be accounted for in a probabilistic manner. The developed quality metric is then used to optimize a camera network for patient tracking during tomotherapy.