反復的な連続座標精緻化によるリアルタイムロボット特徴マッチング
CoReMatch: Recurrent Continuous Coordinate Refinement for Real-Time Robotic Feature Matching
固定グリッド上の信頼度予測ではなく連続座標空間で対応点を反復的に精緻化する特徴マッチング手法CoReMatchを提案し、組み込みエッジ機器でEfficient LoFTRより高精度かつ10倍以上高速に動作することを示した。
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著者: Robert J. Wang, Charles Ling, Boyu Wang
分類: cs.RO, cs.CV
原文アブストラクト
Feature matching underpins many robotic perception tasks, including visual localization, mapping, SLAM, and autonomous navigation. Existing semi-dense matchers predict confidence on fixed grids, limiting correspondences to predefined locations and requiring costly high-resolution grids for precise localization. We present CoReMatch (Coordinate Refinement Match), a recurrent framework that refines correspondences directly in continuous coordinate space rather than predicting discrete confidence maps. CoReMatch progressively improves correspondence estimates by propagating information across iterations. At the coarse stage, a Deep Aggregated Transformer captures global context and iteratively updates correspondence coordinates, while a lightweight regression head further sharpens their localization. Experiments on indoor and outdoor benchmarks show that CoReMatch strikes a strong balance between accuracy and efficiency, outperforming Efficient LoFTR in accuracy while running more than 10x faster on embedded edge hardware. These results demonstrate that recurrent coordinate refinement offers an efficient and scalable approach to real-time feature matching for robotic perception.
