Deep-ARViM: A Perceptual Visibility Metric and Benchmark for Augmented Reality Displays

In optical see-through augmented reality (OST-AR), virtual content is optically superimposed onto the real world, so its visibility depends jointly on the rendered foreground and the background light field. This makes visibility prediction a perceptual primitive for AR: without it, systems cannot reliably reason about display brightness, virtual object placement and rendering, or power consumption under real viewing conditions. Yet existing perceptual metrics were designed for image distortion or structural similarity. Here, we present Deep-ARViM (Augmented Reality Visibility Metric), to our knowledge the first learned perceptual metric explicitly designed to capture content visibility in OST-AR. We collect a large psychophysical dataset with two complementary tasks: 67,984 two-alternative forced-choice (2AFC) judgments and 21,027 visibility matching responses from more than 550 observers, spanning diverse egocentric backgrounds, foreground contents, and luminance conditions. We formulate visibility as a distance between the composited image and the standalone background, and evaluate both hand-crafted features and adapted pretrained vision backbones on their ability to predict human responses. Our best learned models achieve on par performance with an individual human observer in predicting others' 2AFC responses, recover a graded visibility scale that aligns with independent subjective ratings, and transfer zero-shot to quantitative visibility matching. Scientifically, these results suggest that AR visibility is well described by a shared latent perceptual scale that can be recovered from inexpensive binary supervision and generalized across psychophysical tasks. Practically, Deep-ARViM provides a standardized benchmark and a path toward perceptually grounded AR display adaptation.