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In this paper we introduce EfficientPose, a new approach for 6D object poseestimation. Our method is highly accurate, efficient and scalable over a widerange of computational resources. Moreover, it can detect the 2D bounding boxof multiple objects and instances as well as estimate their full 6D poses in asingle shot. This eliminates the significant increase in runtime when dealingwith multiple objects other approaches suffer from. These approaches aim tofirst detect 2D targets, e.g. keypoints, and solve a Perspective-n-Pointproblem for their 6D pose for each object afterwards. We also propose a novelaugmentation method for direct 6D pose estimation approaches to improveperformance and generalization, called 6D augmentation. Our approach achieves anew state-of-the-art accuracy of 97.35% in terms of the ADD(-S) metric on thewidely-used 6D pose estimation benchmark dataset Linemod using RGB input, whilestill running end-to-end at over 27 FPS. Through the inherent handling ofmultiple objects and instances and the fused single shot 2D object detection aswell as 6D pose estimation, our approach runs even with multiple objects(eight) end-to-end at over 26 FPS, making it highly attractive to many realworld scenarios. Code will be made publicly available athttps://github.com/ybkscht/EfficientPose.

EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach