Liu, LiuLi, HongdongDai, YuchaoO'Conner, Lisa2024-02-04October 22978-1-5386-1032-9http://hdl.handle.net/1885/313125Given an image of a street scene in a city, this paper develops a new method that can quickly and precisely pinpoint at which location (as well as viewing direction) the image was taken, against a pre-stored large-scale 3D point-cloud map of the city. We adopt the recently developed 2D-3D direct feature matching framework for this task [23,31,32,42-44]. This is a challenging task especially for large-scale problems. As the map size grows bigger, many 3D points in the wider geographical area can be visually very similar-or even identical-causing severe ambiguities in 2D-3D feature matching. The key is to quickly and unambiguously find the correct matches between a query image and the large 3D map. Existing methods solve this problem mainly via comparing individual features' visual similarities in a local and per feature manner, thus only local solutions can be found, inadequate for large-scale applications. In this paper, we introduce a global method which harnesses global contextual information exhibited both within the query image and among all the 3D points in the map. This is achieved by a novel global ranking algorithm, applied to a Markov network built upon the 3D map, which takes account of not only visual similarities between individual 2D-3D matches, but also their global compatibilities (as measured by co-visibility) among all matching pairs found in the scene. Tests on standard benchmark datasets show that our method achieved both higher precision and comparable recall, compared with the state-of-the-art.This work was supported by China Scholarship Council (201506290131), ARC grants (DP120103896, LP100100588, CE140100016, DE140100180), Australia ARC Centre of Excellence Program on Robotic Vision, NICTA (Data61), Natural Science Foundation of China (61420106007, 61473230, 61374023), State Key Laboratory of Geo-information Engineering (NO.SKLGIE2015- M-3-4) and Aviation Fund of China (2014ZC53030).application/pdfen-AU© 2017 IEEE201710.1109/ICCV.2017.2602022-10-02