M. Bui, S. Albarqouni, S. Ilic, N. Navab Scene Coordinate and Correspondence Learning for Image-Based Localization Proceedings of the British Machine Vision Conference, 3-6 September, 2018, Newcastle (Oral presentation) (bib)
	Scene coordinate regression has become an essential part of current camera relocalization methods. Different versions in the form of 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 scene coordinates pixel-wise for a given RGB image 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 resulting in boosting 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.
	This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.