Robust feature descriptors are necessary for computer vision applications such as image matching, photogrammetric three-dimensional (3D) reconstructions, and simultaneous localisation and mapping (SLAM). While most state-of-the-art feature descriptors are invariant to image transformations (such as translation, rotation, and scale) the majority lack stability in tracking points over large 3D perspective transformations. One successful method to solving these large perspective changes is by simulating affine tilts on the latitude and longitude axes of an image. These simulated tilts create greater invariance to changes in 3D perspective. To demonstrate the widespread efficacy of this approach, this paper applies affine simulation to seven state-of-the-art descriptors in OpenCV and to two of the enhanced OpenCV descriptors in OpenMVG to 5 datasets that all include large affine conditions. Invariant descriptors were able to achieve up to a three-fold increase in feature tracking.