We used a neurobiologically inspired model of simulated prosthetic vision in an immersive virtual reality environment to test the relative importance of semantic edges and relative depth cues to support the ability to avoid obstacles and identify objects.

Rather than aiming to represent the visual scene as naturally as possible, a Smart Bionic Eye could provide visual augmentations through the means of artificial intelligence–based scene understanding, tailored to specific real-world tasks that are known to affect the quality of life of people who are blind.

We present a systematic literature review of 216 publications from 109 different venues assessing the potential of XR technology to serve as not just a visual accessibility aid but also as a tool to study perception and behavior in people with low vision and blind people whose vision was restored with a neuroprosthesis.

We combined deep learning-based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision.