Transformative reality: Augmented reality for visual prostheses
Abstract
Visual prostheses such as retinal implants provide bionic vision that is limited in spatial and intensity resolution. This limitation is a fundamental challenge of bionic vision as it severely truncates salient visual information. We propose to address this challenge by performing real time transformations of visual and non-visual sensor data into symbolic representations that are then rendered as low resolution vision; a concept we call Transformative Reality. For example, a depth camera allows the detection of empty ground in cluttered environments that is then visually rendered as bionic vision to enable indoor navigation. Such symbolic representations are similar to virtual content overlays used in Augmented Reality but are registered to the 3D world via the user's sense of touch. Preliminary user trials, where a head mounted display artificially constrains vision to a 25×25 grid of binary dots, suggest that Transformative Reality provides practical and significant improvements over traditional bionic vision in tasks such as indoor navigation, object localisation and people detection.
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The interpretability of low-resolution vision could be enhanced through information extraction, while reducing the recipient's cognitive load.
“The interpretability of low-resolution vision could be enhanced through information extraction, while at the same time reducing the recipient’s cognitive load.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [69]
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Robust Real-Time Face Detection
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The sensations produced by electrical stimulation of the visual cortex
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Simulating prosthetic vision: I. Visual models of phosphenes
Chen, S. C., Suaning, G. J., Morley, J. W., & Lovell, N. H. (2009) · Vision Research · doi:10.1016/j.visres.2009.02.003 · Cited by 172 · see citing works
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Going beyond vision to improve bionic vision
Li, W. H., Tang, T. J. J., & Lui, W. L. D. (2013) · 2013 IEEE International Conference on Image Processing · doi:10.1109/icip.2013.6738320 · Cited by 7 · see citing works
Self-citationDeep Learning--Based Scene Simplification for Bionic Vision
Han, N., Srivastava, S., Xu, A., Klein, D., & Beyeler, M. (2021) · arXiv · doi:10.48550/arxiv.2102.00297 · Cited by 4 · see citing works
Visual Prostheses in the Era of Artificial Intelligence Technology
Sarbout, I., Gungor, A., Ounissi, M., Zaher, S., Ptito, M., Kupers, R., Racoceanu, D., & Milea, D. (2025) · Eye and Brain · doi:10.2147/eb.s524322 · Cited by 3 · see citing works
“69 Indeed, applying AI to optimize the highly constrained visual information flow conveyed to the blind might provide valuable benefits for the user.”
- Authors
- Lui, W. L. D., Browne, D., Kleeman, L., Drummond, T., & Li, W. H.
- Affiliations
- Monash University (AU)
- Venue
- 2011 10th IEEE International Symposium on Mixed and Augmented Reality
- Publisher
- IEEE
- Published
- 2011-10
- Pages
- 253-254
- ISSN
- 1554-7868 (ISSN-L)
- OpenAlex
- W2089337390
- Cited by
- 12
- References
- 4
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 2 · 2021: 3 · 2015: 1 · 2014: 1 · 2013: 2 · 2012: 1
- FWCI
- 0.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- retinal prosthesis, bionic vision, augmented reality, symbolic representation, depth camera, indoor navigation, object localization, human detection, head-mounted displays
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