A photon transfer curve (PTC) is used to determine fundamental detector noise parameters such as read noise, conversion gain, and fixed pattern noise. Here, the method for determining a PTC is expanded to include 3D noise parameters. 3D noise PTC provides more insight into detector noise and is treated as the next logical step to classical PTC. However, it induces several new challenges in analyzing the results, specifically the fitting of seven, or more, variance curves compared to the one (total variance) or two (temporal and fixed pattern variance) prior. Therefore, a general measurement model is created, which provides a new method to separate out all the classical terms, such as DSNU and PRNU, but can also handle high gain cameras with a noise factor. This method is then verified using Monte Carlo simulations and applied to a commercial machine vision camera. In addition, the effects of lens vignetting and non-uniformity correction (NUC) are explored, along with a comparison of the single pixel PTC.