  We propose a single-stage, category-level 6-DoF pose estimation algorithm
that simultaneously detects and tracks instances of objects within a known
category. Our method takes as input the previous and current frame from a
monocular RGB video, as well as predictions from the previous frame, to predict
the bounding cuboid and 6-DoF pose (up to scale). Internally, a deep network
predicts distributions over object keypoints (vertices of the bounding cuboid)
in image coordinates, after which a novel probabilistic filtering process
integrates across estimates before computing the final pose using PnP. Our
framework allows the system to take previous uncertainties into consideration
when predicting the current frame, resulting in predictions that are more
accurate and stable than single frame methods. Extensive experiments show that
our method outperforms existing approaches on the challenging Objectron
benchmark of annotated object videos. We also demonstrate the usability of our
work in an augmented reality setting.
