  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.
