Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Reality
Yuhao Zhu, Ethan Chen, Colin Hascup, Yukang Yan, Gaurav Sharma

TL;DR
This paper introduces an augmented reality system that reconstructs a 3D color space for color vision deficient individuals, enabling them to distinguish and recognize colors through interactive perceptual shifts and structured training.
Contribution
The novel system reconstructs a 3D color perception space for CVD users using AR, allowing interactive color recognition and learning of color distinctions.
Findings
Colors become distinguishable under rotational shifts in the AR system.
Users learn to resolve color confusions with modest training.
Positive user feedback in real-world color recognition scenarios.
Abstract
We propose an assistive technology that helps individuals with Color Vision Deficiencies (CVD) to recognize/name colors. A dichromat's color perception is a reduced two-dimensional (2D) subset of a normal trichromat's three dimensional color (3D) perception, leading to confusion when visual stimuli that appear identical to the dichromat are referred to by different color names. Using our proposed system, CVD individuals can interactively induce distinct perceptual changes to originally confusing colors via a computational color space transformation. By combining their original 2D precepts for colors with the discriminative changes, a three dimensional color space is reconstructed, where the dichromat can learn to resolve color name confusions and accurately recognize colors. Our system is implemented as an Augmented Reality (AR) interface on smartphones, where users interactively…
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