Adversarial networks for camera pose regression and refinement
Bui M, Baur C, Navab N, Ilic S, Albarqouni S (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 3778-3787
Conference Proceedings Title: Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
Event location: Seoul, KOR
ISBN: 9781728150239
Abstract
Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging task. To address this problem, we introduce a novel framework based, in its core, on the idea of implicitly learning the joint distribution of RGB images and their corresponding camera poses using a discriminator network and adversarial learning. Our method allows not only to regress the camera pose from a single image, however, also offers a solely RGB-based solution for camera pose refinement using the discriminator network. Further, we show that our method can effectively be used to optimize the predicted camera poses and thus improve the localization accuracy. To this end, we validate our proposed method on the publicly available 7-Scenes dataset improving upon the results of direct camera pose regression methods.
Involved external institutions
Germany (DE)How to cite
APA:
Bui, M., Baur, C., Navab, N., Ilic, S., & Albarqouni, S. (2019). Adversarial networks for camera pose regression and refinement. In Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 (pp. 3778-3787). Seoul, KOR: Institute of Electrical and Electronics Engineers Inc..
MLA:
Bui, Mai, et al. "Adversarial networks for camera pose regression and refinement." Proceedings of the 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019, Seoul, KOR Institute of Electrical and Electronics Engineers Inc., 2019. 3778-3787.
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