SemLight: Distilled Semantic–Geometric Fusion for Efficient Local Feature Matching

Robust and efficient local feature matching remains a bot-tleneck for real-time visual localization, especially in environments withlow texture, repetitive structures, or illumination changes. Most priorapproaches model either appearance or geometry in isolation, failing toexploit the semantic–geometric interactions crucial for disambiguatingchallenging matches. To address this limitation, we propose SemLight,built on the insight that compact semantic cues can serve as a lightweightprior for resolving matching ambiguity under challenging conditions.Specifically, high-level semantic priors are first distilled into compact,matching-oriented descriptors via a task-aware student network. Thesedescriptors then conditionally re-weight appearance and geometric chan-nels locally, enabling efficient disambiguation with minimal computa-tional overhead. Extensive experiments on relative pose estimation, ho-mography estimation, and visual localization demonstrate state-of-the-art performance. Under comparable computational cost to XFeat, ourmethod achieves a 17.8% relative improvement in AUC@5 over XFeat(50.2 vs. 42.6).