# Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Reality

arXiv: https://arxiv.org/abs/2408.01895 · HTML: https://mlsanity.com/paper/2408.01895/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 4 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Color-Perception-Guided Display Power Reduction for Virtual Reality](https://mlsanity.com/paper/2209.07610/) (2022)
- [Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data](https://mlsanity.com/paper/1712.01662/) (2017)
- [Modelling, Measuring and Compensating Color Weak Vision](https://mlsanity.com/paper/1510.06507/) (2015)
- [An Adaptive Fuzzy-Based System to Simulate, Quantify and Compensate Color Blindness](https://mlsanity.com/paper/1711.10662/) (2017)

## Abstract (arXiv)

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 control the rotation through swipe gestures and observe the induced color shifts in the camera view or in a displayed image. Through psychophysical experiments and a longitudinal user study, we demonstrate that such rotational color shifts have discriminative power (initially confusing colors become distinct under rotation) and exhibit structured perceptual shifts dichromats can learn with modest training. The AR App is also evaluated in two real-world scenarios (building with lego blocks and interpreting artistic works); users all report positive experience in using the App to recognize object colors that they otherwise could not.