Fringe projection measurement techniques offer fast, non-contact measurements of the surface form of manufactured parts at relatively low cost. Recent advances in fringe projection have reduced measurement errors from effects such as multiple surface reflections and projector defocus. However, there is no standardised calibration framework for fringe projection systems and an uncertainty estimation of surface measurements is rarely carried out in practice. A calibration framework for estimating spatial frequency-dependent measurement uncertainty built on solid theoretical foundations is required. To move towards traceable surface measurement using fringe projection techniques, we are developing a measurement model to accurately predict the captured image and include all major uncertainty contributors, i.e. a virtual fringe projection system. The first step of the model is to calculate the optical field distribution using the three-dimensional optical transfer function of the projector. Next, a camera image is built up using a ray-tracing model to probe the optical field distribution at the measurement surface boundary. The results are compared to an experimental fringe projection system. The intention is to use this model within a Monte-Carlo framework to move towards estimating the uncertainty at each point-cloud data point.