  In this paper, we introduce a rotational primitive prediction based 6D object
pose estimation using a single image as an input. We solve for the 6D object
pose of a known object relative to the camera using a single image with
occlusion. Many recent state-of-the-art (SOTA) two-step approaches have
exploited image keypoints extraction followed by PnP regression for pose
estimation. Instead of relying on bounding box or keypoints on the object, we
propose to learn orientation-induced primitive so as to achieve the pose
estimation accuracy regardless of the object size. We leverage a Variational
AutoEncoder (VAE) to learn this underlying primitive and its associated
keypoints. The keypoints inferred from the reconstructed primitive image are
then used to regress the rotation using PnP. Lastly, we compute the translation
in a separate localization module to complete the entire 6D pose estimation.
When evaluated over public datasets, the proposed method yields a notable
improvement over the LINEMOD, the Occlusion LINEMOD, and the YCB-Video dataset.
We further provide a synthetic-only trained case presenting comparable
performance to the existing methods which require real images in the training
phase.
