Scene coordinate and correspondence learning for image-based localization

Mai Bui, Shadi Albarqouni, Slobodan Ilic, Nassir Navab

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations


Scene coordinate regression has become an essential part of current camera relocalization methods. Different versions, such as regression forests and deep learning methods, have been successfully applied to estimate the corresponding camera pose given a single input image. In this work, we propose to regress the scene coordinates pixel-wise for a given RGB image by using deep learning. Compared to the recent methods, which usually employ RANSAC to obtain a robust pose estimate from the established point correspondences, we propose to regress confidences of these correspondences, which allows us to immediately discard erroneous predictions and improve the initial pose estimates. Finally, the resulting confidences can be used to score initial pose hypothesis and aid in pose refinement, offering a generalized solution to solve this task.

Original languageEnglish
StatePublished - 2019
Event29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom
Duration: 3 Sep 20186 Sep 2018


Conference29th British Machine Vision Conference, BMVC 2018
Country/TerritoryUnited Kingdom


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