用星座匹配法解决眼球反光点识别，提升多光源眼动追踪的可复现性
Night Eyes: A Reproducible Framework for Constellation-Based Corneal Reflection Matching
- 将多个反光点视为有结构的星座，通过几何布局匹配实现定位
- 在公开数据集上噪声环境下仍能保持反光点身份稳定对应
- 开源代码与评测工具，支持透明复现和跨设备对比
角膜反光点检测在瞳孔-角膜反光眼动追踪中至关重要，但实际常作为嵌入大系统的启发式方法，导致不同硬件配置下难以复现。本文提出一种基于二维几何的星座匹配流水线，专注于可复现性与清晰评估。受迷失太空中的星体识别启发，将反光点视为有结构的星座而非独立斑点。提出相似性-布局对齐（SLA）方法，适应多LED眼动追踪的特定约束。该框架结合了可控过检测、自适应候选回退、外观感知评分及可选语义布局先验，同时明确分离检测与对应关系。在公开多光源数据集上验证，系统在噪声条件下仍能保持稳定的标识对应。我们发布代码、预设参数和评估脚本，以支持透明复现、比较与数据集标注。
原文摘要 · Abstract (English)
Corneal reflection (glint) detection plays an important role in pupil-corneal reflection (P-CR) eye tracking, but in practice it is often handled as heuristics embedded within larger systems, making reproducibility difficult across hardware setups. We introduce a 2D geometry-driven, constellation-based pipeline for mulit-glint detection and matching, focusing on reproducibility and clear evaluation. Inspired by lost-in-space star identification, we treat glints as structured constellations rather than independent blobs. We propose a Similarity-Layout Alignment (SLA) procedure which adapts constellation matching to the specific constraints of multi-LED eye tracking. The framework brings together controlled over-detection, adaptive candidate fallback, appearance-aware scoring, and optional semantic layout priors while keeping detection and correspondence explicitly separated. Evaluated on a public multi-LED dataset, the system provides stable identity-preserving correspondence under noisy conditions. We release code, presets, and evaluation scripts to enable transparent replication, comparison, and dataset annotation.
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