  6D object pose estimation is an important task that determines the 3D
position and 3D rotation of an object in camera-centred coordinates. By
utilizing such a task, one can propose promising solutions for various problems
related to scene understanding, augmented reality, control and navigation of
robotics. Recent developments on visual depth sensors and low-cost availability
of depth data significantly facilitate object pose estimation. Using depth
information from RGB-D sensors, substantial progress has been made in the last
decade by the methods addressing the challenges such as viewpoint variability,
occlusion and clutter, and similar looking distractors. Particularly, with the
recent advent of convolutional neural networks, RGB-only based solutions have
been presented. However, improved results have only been reported for
recovering the pose of known instances, i.e., for the instance-level object
pose estimation tasks. More recently, state-of-the-art approaches target to
solve object pose estimation problem at the level of categories, recovering the
6D pose of unknown instances. To this end, they address the challenges of the
category-level tasks such as distribution shift among source and target
domains, high intra-class variations, and shape discrepancies between objects.
