Scene coordinate and correspondence learning for image-based localization
Bui M, Albarqouni S, Ilic S, Navab N (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: BMVA Press
Conference Proceedings Title: British Machine Vision Conference 2018, BMVC 2018
Event location: Newcastle, GBR
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Bui, M., Albarqouni, S., Ilic, S., & Navab, N. (2019). Scene coordinate and correspondence learning for image-based localization. In British Machine Vision Conference 2018, BMVC 2018. Newcastle, GBR: BMVA Press.
MLA:
Bui, Mai, et al. "Scene coordinate and correspondence learning for image-based localization." Proceedings of the 29th British Machine Vision Conference, BMVC 2018, Newcastle, GBR BMVA Press, 2019.
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