X-Ray PoseNet: 6 DoF pose estimation for mobile x-ray devices
Bui M, Albarqouni S, Schrapp M, Navab N, Ilic S (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 1036-1044
Conference Proceedings Title: Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
Event location: Santa Rosa, CA, USA
ISBN: 9781509048229
Abstract
Precise reconstruction of 3D volumes from X-ray projections requires precisely pre-calibrated systems where accurate knowledge of the systems geometric parameters is known ahead. However, when dealing with mobile X-ray devices such calibration parameters are unknown. Joint estimation of the systems calibration parameters and 3d reconstruction is a heavily unconstrained problem, especially when the projections are arbitrary. In industrial applications, that we target here, nominal CAD models of the object to be reconstructed are usually available. We rely on this prior information and employ Deep Learning to learn the mapping between simulated X-ray projections and its pose. Moreover, we introduce the reconstruction loss in addition to the pose loss to further improve the reconstruction quality. Finally, we demonstrate the generalization capabilities of our method in case where poses can be learned on instances of the objects belonging to the same class, allowing pose estimation of unseen objects from the same category, thus eliminating the need for the actual CAD model. We performed exhaustive evaluation demonstrating the quality of our results on both synthetic and real data.
Involved external institutions
Germany (DE)How to cite
APA:
Bui, M., Albarqouni, S., Schrapp, M., Navab, N., & Ilic, S. (2017). X-Ray PoseNet: 6 DoF pose estimation for mobile x-ray devices. In Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017 (pp. 1036-1044). Santa Rosa, CA, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Bui, Mai, et al. "X-Ray PoseNet: 6 DoF pose estimation for mobile x-ray devices." Proceedings of the 17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017, Santa Rosa, CA, USA Institute of Electrical and Electronics Engineers Inc., 2017. 1036-1044.
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