GeoDistill-Refine: Silhouette-First Geometry Distillation for Annotation-Free Spacecraft Segmentation
Foundation segmentation models can provide supervision for spacecraft imagery without manual training masks, but their predictions vary with textual prompts and may contain geometric errors that are amplified during distillation. This paper presents GeoDistill-Refine, a two-stage framework that transfers offline SAM 3 pseudo-masks to a compact segmentation network. Six fixed prompts are fused by an unweighted 50% vote to stabilize the teacher output. The student first learns the foreground silho
Record details
Published: 7 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Children & education
Retrieved: 10 August 2026
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ethics.ai (7 August 2026), “GeoDistill-Refine: Silhouette-First Geometry Distillation for Annotation-Free Spacecraft Segmentation,” evidence record 17918, https://ethics.ai/record/17918 (originally published by arXiv cs.AI).
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