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Optical see-through Augmented Reality (OST-AR) is a developing technology with exciting applications including medicine, industry, education, and entertainment. OST-AR creates a mix of virtual and real using an optical combiner that blends images and graphics with the real-world environment. Such an overlay of visual information is simultaneously futuristic and familiar: like the sci-fi navigation and communication interfaces in movies, but also much like banal reflections in glass windows. OSTAR’s transparent displays cause background bleed-through, which distorts color and contrast, yet virtual content is usually easily understandable. Perceptual scission, or the cognitive separation of layers, is an important mechanism, influenced by transparency, depth, parallax, and more, that helps us see what is real and what is virtual. In examples from Pepper’s Ghost, veiling luminance, mixed material modes, window shopping, and today’s OST-AR systems, transparency and scission provide surprising – and ordinary – results. Ongoing psychophysical research is addressing perceived characteristics of color, material, and images in OST-AR, testing and harnessing the perceptual effects of transparency and scission. Results help both understand the visual mechanisms and improve tomorrow’s AR systems.
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Color in Layers: From Pepper’s Ghost to Augmented Reality
Optical see-through AR presents virtual objects to a user through a transparent display that blends them with the real-world environment. This is simultaneously novel and familiar: beam splitters have been used for ghostly visual effects, and yet the mechanism is exactly the same as the reflections in an everyday window. The history of theatrical visual effects leads through a series of vision science experiments and now to research on the perception of transparent AR systems. Still, there is a tension in the perception of AR stimuli: users of AR seem to be able to separate, or scission, the layers of virtual and real, depending on their understanding of the scene and its visual characteristics.
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- Award ID(s):
- 1942755
- PAR ID:
- 10341847
- Editor(s):
- Funt, Brian; Kingsburgh, Robin
- Date Published:
- Journal Name:
- AIC Toronto 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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