Fringe projection techniques offer fast, non-contact measurements of the surface form of manufactured parts. Fringe projection has seen successful implementation in the automotive, aerospace and medical industries. Recently, advances in fringe projection have reduced the sensitivity of the measurement system to effects such as multiple surface reflections and projector defocus. Typically, the measurement method is altered to optimise the system for specific measurement conditions, without any regard for quantifying the effects of influence factors. Furthermore, there is no standardised calibration framework for fringe projection systems and uncertainty evaluation of surface measurements is rarely carried out in practice, which places some restrictions on the use of this technique in manufacturing industry. Fringe projection systems detect the intensity of a projected fringe pattern that is reflected from the surface to be measured. Any process that alters the intensity of this pattern received by the camera will change the measurement outcome. Therefore, fringe projection systems typically have many influence factors that affect the measurement outcome, including the surface characteristics (e.g. optical properties and topography), imaging optics (e.g. defocus and aberrations) and external factors (e.g. ambient light intensity level, mechanical vibration and temperature). The complexity of the measurement model makes current calibration methods given in ISO 15530 (for contact coordinate measuring machines) unsuitable for fringe projection. Additionally, it is unclear how to apply the calibration method in ISO 25178 for areal surface topography measuring instruments. A calibration framework for estimating spatial frequency dependent measurement uncertainty built on solid theoretical foundations is required. To move towards a 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. The first step of the model is to describe the optical field distribution within the projection volume of the projector by considering its point spread function in three dimensions. The optical field distribution is sampled at surface locations, using a ray tracing algorithm to map intensity values to corresponding camera pixels. The results are validated by comparing to an experimental fringe projection system with carefully controlled parameters. The intention is to use this simulation within a Monte-Carlo framework to create an uncertainty map of the phase image that can be used to estimate the uncertainty at each point-cloud data point. Additionally, the model will give insights into the relationships between influence factors, allowing the implementation of improvements to fringe projection systems.