  Inferring the 6DoF pose of an object from a single RGB image is an important
but challenging task, especially under heavy occlusion. While recent approaches
improve upon the two stage approaches by training an end-to-end pipeline, they
do not leverage local and global constraints. In this paper, we propose
pairwise feature extraction to integrate local constraints, and triplet
regularization to integrate global constraints for improved 6DoF object pose
estimation. Coupled with better augmentation, our approach achieves state of
the art results on the challenging Occlusion Linemod dataset, with a 9%
improvement over the previous state of the art, and achieves competitive
results on the Linemod dataset.
