ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation

Su Y, Saleh M, Fetzer T, Rambach J, Navab N, Busam B, Stricker D, Tombari F (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Book Volume: 2022-June

Pages Range: 6728-6738

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: New Orleans, LA, USA

ISBN: 9781665469463

DOI: 10.1109/CVPR52688.2022.00662

Abstract

Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r.t. ADD(-S) metric, even surpassing RGB-D based methods in some cases.

Involved external institutions

Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) GmbH DE Germany (DE) Technische Universität München (TUM) DE Germany (DE) Technische Universität Kaiserslautern DE Germany (DE)

How to cite

APA:

Su, Y., Saleh, M., Fetzer, T., Rambach, J., Navab, N., Busam, B.,... Tombari, F. (2022). ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 6728-6738). New Orleans, LA, USA: IEEE Computer Society.

MLA:

Su, Yongzhi, et al. "ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 6728-6738.

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