  Visual neuroprostheses (bionic eyes) have the potential to treat degenerative
eye diseases that often result in low vision or complete blindness. These
devices rely on an external camera to capture the visual scene, which is then
translated frame-by-frame into an electrical stimulation pattern that is sent
to the implant in the eye. To highlight more meaningful information in the
scene, recent studies have tested the effectiveness of deep-learning based
computer vision techniques, such as depth estimation to highlight nearby
obstacles (DepthOnly mode) and semantic edge detection to outline important
objects in the scene (EdgesOnly mode). However, nobody has attempted to combine
the two, either by presenting them together (EdgesAndDepth) or by giving the
user the ability to flexibly switch between them (EdgesOrDepth). Here, we used
a neurobiologically inspired model of simulated prosthetic vision (SPV) in an
immersive virtual reality (VR) environment to test the relative importance of
semantic edges and relative depth cues to support the ability to avoid
obstacles and identify objects. We found that participants were significantly
better at avoiding obstacles using depth-based cues as opposed to relying on
edge information alone, and that roughly half the participants preferred the
flexibility to switch between modes (EdgesOrDepth). This study highlights the
relative importance of depth cues for SPV mobility and is an important first
step towards a visual neuroprosthesis that uses computer vision to improve a
user's scene understanding.
