  Estimating the 6-DoF pose of a rigid object from a single RGB image is a
crucial yet challenging task. Recent studies have shown the great potential of
dense correspondence-based solutions, yet improvements are still needed to
reach practical deployment. In this paper, we propose a novel pose estimation
algorithm named CheckerPose, which improves on three main aspects. Firstly,
CheckerPose densely samples 3D keypoints from the surface of the 3D object and
finds their 2D correspondences progressively in the 2D image. Compared to
previous solutions that conduct dense sampling in the image space, our strategy
enables the correspondence searching in a 2D grid (i.e., pixel coordinate).
Secondly, for our 3D-to-2D correspondence, we design a compact binary code
representation for 2D image locations. This representation not only allows for
progressive correspondence refinement but also converts the correspondence
regression to a more efficient classification problem. Thirdly, we adopt a
graph neural network to explicitly model the interactions among the sampled 3D
keypoints, further boosting the reliability and accuracy of the
correspondences. Together, these novel components make CheckerPose a strong
pose estimation algorithm. When evaluated on the popular Linemod, Linemod-O,
and YCB-V object pose estimation benchmarks, CheckerPose clearly boosts the
accuracy of correspondence-based methods and achieves state-of-the-art
performances. Code is available at https://github.com/RuyiLian/CheckerPose.
