  The spatially-varying field of the human visual system has recently received
a resurgence of interest with the development of virtual reality (VR) and
neural networks. The computational demands of high resolution rendering desired
for VR can be offset by savings in the periphery, while neural networks trained
with foveated input have shown perceptual gains in i.i.d and o.o.d
generalization. In this paper, we present a technique that exploits the CUDA
GPU architecture to efficiently generate Gaussian-based foveated images at high
definition (1920x1080 px) in real-time (165 Hz), with a larger number of
pooling regions than previous Gaussian-based foveation algorithms by several
orders of magnitude, producing a smoothly foveated image that requires no
further blending or stitching, and that can be well fit for any contrast
sensitivity function. The approach described can be adapted from Gaussian
blurring to any eccentricity-dependent image processing and our algorithm can
meet demand for experimentation to evaluate the role of spatially-varying
processing across biological and artificial agents, so that foveation can be
added easily on top of existing systems rather than forcing their redesign
(emulated foveated renderer). Altogether, this paper demonstrates how a GPU,
with a CUDA block-wise architecture, can be employed for radially-variant
rendering, with opportunities for more complex post-processing to ensure a
metameric foveation scheme. Code is provided.
