Authors:
Dominik Penk
1
;
Maik Horn
2
;
Christoph Strohmeyer
2
;
Frank Bauer
1
and
Marc Stamminger
1
Affiliations:
1
Chair of Visual Computing, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstraße 11, Erlangen, Germany
;
2
Schaeffler Technologies AG & Co. KG, Industriestraße 1-3, Herzogenaurach, Germany
Keyword(s):
6D Pose Estimation, Object Tracking, Depth Simulation, Machine Learning, Robust Estimators.
Abstract:
We propose a novel pipeline to construct a learning based 6D object pose tracker, which is solely trained on synthetic depth images. The only required input is a (geometric) CAD model of the target object. Training data is synthesized by rendering stereo images of the CAD model, in front of a large variety of backgrounds generated by point-based re-renderings of prerecorded background scenes. Finally, depth from stereo is applied in order to mimic the behavior of depth sensors. The synthesized training input generalizes well to real-world scenes, but we further show how to improve real-world inference using robust estimators to counteract the errors introduced by the sim-to-real transfer. As a result, we show that our 6D pose trackers achieve state-of-the-art results without any annotated real-world data, solely based on a CAD-model of the target object.