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arXiv · 2211.08712

Improving Feature-based Visual Localization by Geometry-Aided Matching

Hailin Yu·Youji Feng·Weicai Ye·Mingxuan Jiang·Hujun Bao·Guofeng Zhang
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Abstract

Feature matching is crucial in visual localization, where 2D-3D correspondence plays a major role in determining the accuracy of camera pose. A sufficient number of well-distributed 2D-3D correspondences is essential for accurate pose estimation due to noise. However, existing 2D-3D feature matching methods rely on finding nearest neighbors in the feature space and removing outliers using hand-crafted heuristics, which may lead to potential matches being missed or the correct matches being filtered out. In this work, we propose a novel method called Geometry-Aided Matching (GAM), which incorporates both appearance information and geometric context to address this issue and to improve 2D-3D feature matching. GAM can greatly boost the recall of 2D-3D matches while maintaining high precision. We apply GAM to a new hierarchical visual localization pipeline and show that GAM can effectively improve the robustness and accuracy of localization. Extensive experiments show that GAM can find more real matches than hand-crafted heuristics and learning baselines. Our proposed localization method achieves state-of-the-art results on multiple visual localization datasets. Experiments on Cambridge Landmarks dataset show that our method outperforms the existing state-of-the-art methods and is six times faster than the top-performed method. The source code is available at https://github.com/openxrlab/xrlocalization.

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Hailin Yu, Youji Feng, Weicai Ye, Mingxuan Jiang, Hujun Bao, Guofeng Zhang. 2022-11-16. Improving Feature-based Visual Localization by Geometry-Aided Matching. https://arxiv.org/abs/2211.08712

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