GeoDistill-Refine: Silhouette-First Geometry Distillation for Annotation-Free Spacecraft Segmentation

Segmenting spacecraft in imagery typically requires manual annotation, but foundation segmentation models can generate pseudo-masks automatically, despite geometric inaccuracies that worsen during distillation. GeoDistill-Refine improves this by stabilizing teacher predictions through prompt fusion and refining a lightweight student network using silhouette, boundary, and shape-based objectives, filtered by a reliability gate. This is directly applicable to space situational awareness, satellite servicing, and space debris tracking, where accurate, annotation-free spacecraft segmentation is valuable. The resulting compact model runs efficiently (1.1ms per image) while improving boundary and region accuracy across multiple spacecraft imagery domains. Paper: https://arxiv.org/abs/2608.07405