  In this paper we introduce EfficientPose, a new approach for 6D object pose
estimation. Our method is highly accurate, efficient and scalable over a wide
range of computational resources. Moreover, it can detect the 2D bounding box
of multiple objects and instances as well as estimate their full 6D poses in a
single shot. This eliminates the significant increase in runtime when dealing
with multiple objects other approaches suffer from. These approaches aim to
first detect 2D targets, e.g. keypoints, and solve a Perspective-n-Point
problem for their 6D pose for each object afterwards. We also propose a novel
augmentation method for direct 6D pose estimation approaches to improve
performance and generalization, called 6D augmentation. Our approach achieves a
new state-of-the-art accuracy of 97.35% in terms of the ADD(-S) metric on the
widely-used 6D pose estimation benchmark dataset Linemod using RGB input, while
still running end-to-end at over 27 FPS. Through the inherent handling of
multiple objects and instances and the fused single shot 2D object detection as
well 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 real
world scenarios. Code will be made publicly available at
https://github.com/ybkscht/EfficientPose.
