Deep model-based 6d pose refinement in rgb

Manhardt F, Kehl W, Navab N, Tombari F (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 11218 LNCS

Pages Range: 833-849

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Munich, DEU

ISBN: 9783030012632

DOI: 10.1007/978-3-030-01264-9_49

Abstract

We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code at http://campar.in.tum.de/Main/FabianManhardt to ensure reproducibility.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Toyota US United States (USA) (US)

How to cite

APA:

Manhardt, F., Kehl, W., Navab, N., & Tombari, F. (2018). Deep model-based 6d pose refinement in rgb. In Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 833-849). Munich, DEU: Springer Verlag.

MLA:

Manhardt, Fabian, et al. "Deep model-based 6d pose refinement in rgb." Proceedings of the 15th European Conference on Computer Vision, ECCV 2018, Munich, DEU Ed. Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert, Springer Verlag, 2018. 833-849.

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