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Inferring the 6DoF pose of an object from a single RGB image is an importantbut challenging task, especially under heavy occlusion. While recent approachesimprove upon the two stage approaches by training an end-to-end pipeline, theydo not leverage local and global constraints. In this paper, we proposepairwise feature extraction to integrate local constraints, and tripletregularization to integrate global constraints for improved 6DoF object poseestimation. Coupled with better augmentation, our approach achieves state ofthe art results on the challenging Occlusion Linemod dataset, with a 9%improvement over the previous state of the art, and achieves competitiveresults on the Linemod dataset.

End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints