Deep 6-DOF Tracking

We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate, and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded. Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.

Paper

Mathieu Garon and Jean-Fran├žois Lalonde
Deep 6-DOF Tracking
[arXiv:1703.09771 pre-print] [BibTeX]

Code

Code coming soon!

Data

Data coming soon!

Video

Acknowledgements

The authors gratefully acknowledge the following funding sources:

  • FRQ-NT New Researcher Grant 2016NC189939
  • NSERC Discovery Grant RGPIN-2014-05314
  • NVIDIA Corporation with the donation of the Tesla K40 and Titan X GPUs used for this research.
  • REPARTI Strategic Network

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