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.

翻译：角膜反射（闪烁点）检测在瞳孔-角膜反射（P-CR）眼动追踪中具有重要作用，但在实际中常被作为嵌入更大系统中的启发式方法处理，导致跨硬件设置的复现困难。我们提出了一种基于二维几何驱动的星座结构多闪烁点检测与匹配流水线，重点关注可复现性与清晰评估。受失联星体识别技术的启发，我们不再将闪烁点视为独立斑块，而是将其视为结构化的星座模式。我们提出了一种相似性-布局对齐（SLA）方法，该方法将星座匹配适配到多LED眼动追踪的特殊约束条件下。该框架整合了可控过检测、自适应候选回退、外观感知评分以及可选的语义布局先验，同时保持检测与对应关系的明确分离。在公开的多LED数据集上的评估表明，该系统在噪声条件下能够提供稳定的身份保持对应关系。我们公开了代码、预设参数和评估脚本，以支持透明的复现、比较和数据集标注。