TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE
6D Pose Estimation using RGB	LineMOD	E2E6DoF	Mean ADD	86.8	# 13
6D Pose Estimation using RGB	Occlusion LineMOD	E2E6DoF	Mean ADD	47.4	# 8

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

22 Nov 2020 · Anshul Gupta, Joydeep Medhi, Aratrik Chattopadhyay, Vikram Gupta ·

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.

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