Self6D: Self-supervised Monocular 6D Object Pose Estimation
Wang G, Manhardt F, Shao J, Ji X, Navab N, Tombari F (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12346 LNCS
Pages Range: 108-125
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Glasgow, GBR
ISBN: 9783030584511
DOI: 10.1007/978-3-030-58452-8_7
Abstract
6D object pose estimation is a fundamental problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliable 6D pose estimates even from monocular images. Nonetheless, CNNs are identified as being extremely data-driven, and acquiring adequate annotations is oftentimes very time-consuming and labor intensive. To overcome this shortcoming, we propose the idea of monocular 6D pose estimation by means of self-supervised learning, removing the need for real annotations. After training our proposed network fully supervised with synthetic RGB data, we leverage recent advances in neural rendering to further self-supervise the model on unannotated real RGB-D data, seeking for a visually and geometrically optimal alignment. Extensive evaluations demonstrate that our proposed self-supervision is able to significantly enhance the model’s original performance, outperforming all other methods relying on synthetic data or employing elaborate techniques from the domain adaptation realm.
Involved external institutions
China (CN)
Germany (DE)How to cite
APA:
Wang, G., Manhardt, F., Shao, J., Ji, X., Navab, N., & Tombari, F. (2020). Self6D: Self-supervised Monocular 6D Object Pose Estimation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 108-125). Glasgow, GBR: Springer Science and Business Media Deutschland GmbH.
MLA:
Wang, Gu, et al. "Self6D: Self-supervised Monocular 6D Object Pose Estimation." Proceedings of the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, GBR Ed. Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm, Springer Science and Business Media Deutschland GmbH, 2020. 108-125.
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