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The spatially-varying field of the human visual system has recently receiveda resurgence of interest with the development of virtual reality (VR) andneural networks. The computational demands of high resolution rendering desiredfor VR can be offset by savings in the periphery, while neural networks trainedwith foveated input have shown perceptual gains in i.i.d and o.o.dgeneralization. In this paper, we present a technique that exploits the CUDAGPU architecture to efficiently generate Gaussian-based foveated images at highdefinition (1920x1080 px) in real-time (165 Hz), with a larger number ofpooling regions than previous Gaussian-based foveation algorithms by severalorders of magnitude, producing a smoothly foveated image that requires nofurther blending or stitching, and that can be well fit for any contrastsensitivity function. The approach described can be adapted from Gaussianblurring to any eccentricity-dependent image processing and our algorithm canmeet demand for experimentation to evaluate the role of spatially-varyingprocessing across biological and artificial agents, so that foveation can beadded 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-variantrendering, with opportunities for more complex post-processing to ensure ametameric foveation scheme. Code is provided.

CUDA-Optimized real-time rendering of a Foveated Visual System