# Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework

arXiv: https://arxiv.org/abs/2605.09181 · HTML: https://mlsanity.com/paper/2605.09181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 9 May 2026.

## Builds on (most cited first, 6 of 6)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching](https://mlsanity.com/paper/2207.07932/) (2022)
- [RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration](https://mlsanity.com/paper/2404.16017/) (2024)

## Built on by (most cited first, 1 of 1)

- [Modality-Invariant Coarse-to-Fine Retinal Image Registration](https://mlsanity.com/paper/2608.14829/) (2026)

## Abstract (arXiv)

Retinal image-based eye tracking is widely used in ophthalmic imaging and vision science, and is a promising path to deliver higher gaze accuracy than the pupil- and cornea-based approaches commonly used in modern AR/VR devices. Nevertheless, existing retinal tracking algorithms still primarily rely on classical template-matching registration, which can be insufficiently robust to retinal feature variability and real-world imaging conditions. In this work, we propose a novel weakly-supervised, learning-based framework for robust retinal eye tracking. Initial studies demonstrate high accuracy, achieving the 95th-percentile gaze error < 0.45 deg across a cohort of 6 participants.