  We present a challenging and realistic novel dataset for evaluating 6-DOF
object tracking algorithms. Existing datasets show serious
limitations---notably, unrealistic synthetic data, or real data with large
fiducial markers---preventing the community from obtaining an accurate picture
of the state-of-the-art. Using a data acquisition pipeline based on a
commercial motion capture system for acquiring accurate ground truth poses of
real objects with respect to a Kinect V2 camera, we build a dataset which
contains a total of 297 calibrated sequences. They are acquired in three
different scenarios to evaluate the performance of trackers: stability,
robustness to occlusion and accuracy during challenging interactions between a
person and the object. We conduct an extensive study of a deep 6-DOF tracking
architecture and determine a set of optimal parameters. We enhance the
architecture and the training methodology to train a 6-DOF tracker that can
robustly generalize to objects never seen during training, and demonstrate
favorable performance compared to previous approaches trained specifically on
the objects to track.
