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|Title:||A two-stage outlier filtering framework for city-scale localization using 3D SfM point clouds||Authors:||Cheng, Wentao
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2019||Source:||Cheng, W., Chen, K., Lin, W., Goesele, M., Zhang, X., & Zhang, Y. (2019). A two-stage outlier filtering framework for city-scale localization using 3D SfM point clouds. IEEE Transactions on Image Processing, 28(10), 4857-4869. doi:10.1109/TIP.2019.2910662||Journal:||IEEE Transactions on Image Processing||Abstract:||Three-dimensional structure-based localization aims to estimate the six-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 the SfM point clouds. First, we propose a visibility-based outlier filter, which is based on a bipartite visibility graph, to filter outliers on a coarse level. Second, 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 the intrinsic information of the SfM point cloud. It is thus widely applicable to be embedded into the 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.||URI:||https://hdl.handle.net/10356/137993||ISSN:||1941-0042||DOI:||10.1109/TIP.2019.2910662||Rights:||© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2019.2910662||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||Fraunhofer Singapore Journal Articles|
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