Abstract
3D pose estimation is an important problem with many potential applications. However, 3D acquiring annotations for color images is a difficult task. To create training data, the annotating is usually done with the help of markers or a robotic system, which in both cases is very cumbersome, expensive, or sometimes even impossible, especially from color images. Another option is to use synthetic images for training. However, synthetic images do not resemble real images exactly. To bridge this domain gap, Generative Adversarial Networks or transfer learning techniques can be used but, they require some annotated real images to learn the domain transfer. To overcome these problems, we propose a novel approach in this paper. Section II gives a short summary of our approach that uses synthetic data only, and Section III shows some results.
Original language | English |
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Title of host publication | Proceedings of the joint OAGM & ARW Workshop 2019 |
Editors | Andreas Pichler, Peter M. Roth, Robert Slabatnig, Gernot Stübl |
Place of Publication | Graz |
Publisher | Verlag der Technischen Universität Graz |
Number of pages | 1 |
ISBN (Electronic) | 9783851256635 |
DOIs | |
Publication status | Published - 2019 |
Event | OAGM/ARW 2019: ARW & OAGM Workshop 2019 - Steyr, Austria Duration: 9 May 2019 → 10 May 2019 |
Conference
Conference | OAGM/ARW 2019 |
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Country/Territory | Austria |
City | Steyr |
Period | 9/05/19 → 10/05/19 |
Other | Austrian Robotics Workshop and OAGM Workshop 2019 |
Keywords
- 3D Object Pose Estimatio
- 3D Hand Pose Estimation
- Domain Transfer