# Optical Gaze Tracking with Spatially-Sparse Single-Pixel Detectors

arXiv: https://arxiv.org/abs/2009.06875 · HTML: https://mlsanity.com/paper/2009.06875/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Learned holographic light transport](https://mlsanity.com/paper/2108.08253/) (2021)
- [Slippage-robust Gaze Tracking for Near-eye Display](https://mlsanity.com/paper/2210.11637/) (2022)
- [Low Latency Gaze Tracking via Latent Optical Sensing](https://mlsanity.com/paper/2605.17990/) (2026)

## Abstract (arXiv)

Gaze tracking is an essential component of next generation displays for virtual reality and augmented reality applications. Traditional camera-based gaze trackers used in next generation displays are known to be lacking in one or multiple of the following metrics: power consumption, cost, computational complexity, estimation accuracy, latency, and form-factor. We propose the use of discrete photodiodes and light-emitting diodes (LEDs) as an alternative to traditional camera-based gaze tracking approaches while taking all of these metrics into consideration. We begin by developing a rendering-based simulation framework for understanding the relationship between light sources and a virtual model eyeball. Findings from this framework are used for the placement of LEDs and photodiodes. Our first prototype uses a neural network to obtain an average error rate of 2.67deg at 400Hz while demanding only 16mW. By simplifying the implementation to using only LEDs, duplexed as light transceivers, and more minimal machine learning model, namely a light-weight supervised Gaussian process regression algorithm, we show that our second prototype is capable of an average error rate of 1.57deg at 250 Hz using 800 mW.