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A Two-stage Outlier Filtering Framework for City-Scale Localization using 3D SfM Point Clouds

: Cheng, Wentao; Chen, Kan; Lin, Weisi; Goesele, Michael; Zhang, Xinfeng; Zhang, Yabin


IEEE transactions on image processing 28 (2019), No.10, pp.4857-4869
ISSN: 1057-7149
ISSN: 1941-0042
Journal Article
Fraunhofer Singapore ()
Structure-from-Motion (SfM); 3D display; image restoration; evaluation

3D Structure-based localization aims to estimate the 6-DOF camera pose of a query image by means of feature matches against a 3D Structure-from-Motion (SfM) point cloud. For city-scale SfM point clouds with tens of millions of points, it becomes more and more difficult to disambiguate matches. Therefore a 3D Structure-based localization method, which can efficiently handle matches with very large outlier ratios, is needed. We propose a two-stage outlier filtering framework for city-scale localization that leverages both visibility and geometry intrinsics of SfM point clouds. Firstly, we propose a visibility-based outlier filter, which is based on a bipartite visibility graph, to filter outliers on a coarse level. Secondly, we apply a geometry-based outlier filter to generate a set of fine-grained matches with a novel data-driven geometrical constraint for efficient inlier evaluation. The proposed two-stage outlier filtering framework only relies on intrinsic information of a SfM point cloud. It is thus widely applicable to be embedded into existing localization approaches. The experimental results on two real-world datasets demonstrate the effectiveness of the proposed two-stage outlier filtering framework for city-scale localization.