FUSELOC: 大域記述子と局所記述子の融合による視覚位置推定での2D-3Dマッチング曖昧性解消
FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization
局所記述子と大域記述子を重み付き平均で融合し、地理的に近い記述子を特徴空間で近づけることで、直接2D-3Dマッチングの曖昧性を低減し、階層的手法に匹敵する精度を43%少ないメモリと1.6倍の速度で実現した。
著者: Son Tung Nguyen, Alejandro Fontan, Michael Milford, Tobias Fischer
分類: cs.CV, cs.RO
原文アブストラクト
Hierarchical visual localization methods achieve state-of-the-art accuracy but require substantial memory as they need to store all database images. Direct 2D-3D matching requires significantly less memory but suffers from lower accuracy due to the larger and more ambiguous search space. We address this ambiguity by fusing local and global descriptors using a weighted average operator. This operator rearranges the local descriptor space so that geographically nearby local descriptors are closer in the feature space according to the global descriptors. This decreases the number of irrelevant competing descriptors, especially if they are geographically distant, thus increasing the correct matching likelihood. We consistently improve the accuracy over local-only systems, and we achieve performance close to hierarchical methods while using 43\% less memory and running 1.6 times faster. Extensive experiments on four challenging datasets -- Cambridge Landmarks, Aachen Day/Night, RobotCar Seasons, and Extended CMU Seasons -- demonstrate that, for the first time, direct matching algorithms can benefit from global descriptors without compromising computational efficiency. Our code is available at \href{https://github.com/sontung/descriptor-disambiguation}{https://github.com/sontung/descriptor-disambiguation}.
