You Only Look at One: Category-Level Object Representations for Pose
  Estimation From a Single Example
Walter Goodwin Ioannis Havoutis Ingmar Posner
In order to meaningfully interact with the world, robot manipulators must be
able to interpret objects they encounter. A critical aspect of this
interpretation is pose estimation: inferring quantities that describe the
position and orientation of an object in 3D space. Most existing approaches to
pose estimation make limiting assumptions, often working only for specific,
known object instances, or at best generalising to an object category using
large pose-labelled datasets. In this work, we present a method for achieving
category-level pose estimation by inspection of just a single object from a
desired category. We show that we can subsequently perform accurate pose
estimation for unseen objects from an inspected category, and considerably
outperform prior work by exploiting multi-view correspondences. We demonstrate
that our method runs in real-time, enabling a robot manipulator equipped with
an RGBD sensor to perform online 6D pose estimation for novel objects. Finally,
we showcase our method in a continual learning setting, with a robot able to
determine whether objects belong to known categories, and if not, use active
perception to produce a one-shot category representation for subsequent pose
estimation.