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OBJECTIVE	We evaluated a novel visual representation for current and near-term prosthetic vision .
OBJECTIVE	Augmented depth emphasizes ground obstacles and floor-wall boundaries in a depth-based visual representation .
OBJECTIVE	This is achieved by artificially increasing contrast between obstacles and the ground surface via a novel ground plane extraction algorithm specifically designed to preserve low-contrast ground-surface boundaries .
METHODS	The effectiveness of augmented depth was examined in human mobility trials compared against standard intensity-based ( Intensity ) , depth-based ( Depth ) and random ( Random ) visual representations .
METHODS	Eight participants with normal vision used simulated prosthetic vision with 20 phosphenes and eight perceivable brightness levels to traverse a course with randomly placed small and low-contrast obstacles on the ground .
RESULTS	The number of collisions was significantly reduced using augmented depth , compared with intensity , depth and random representations ( 48 % , 44 % and 72 % less collisions , respectively ) .
CONCLUSIONS	These results indicate that augmented depth may enable safe mobility in the presence of low-contrast obstacles with current and near-term implants .
CONCLUSIONS	This is the first demonstration that an augmentation of the scene ensuring key objects are visible may provide better outcomes for prosthetic vision .

