  In this paper, we propose an efficient end-to-end algorithm to tackle the
problem of estimating the 6D pose of objects from a single RGB image. Our
system trains a fully convolutional network to regress the 3D rotation and the
3D translation in region layer. On this basis, a special layer, Collinear
Equation Layer, is added next to region layer to output the 2D projections of
the 3D bounding boxs corners. In the back propagation stage, the 6D pose
network are adjusted according to the error of the 2D projections. In the
detection phase, we directly output the position and pose through the region
layer. Besides, we introduce a novel and concise representation of 3D rotation
to make the regression more precise and easier. Experiments show that our
method outperforms base-line and state of the art methods both at accuracy and
efficiency. In the LineMod dataset, our algorithm achieves less than 18
ms/object on a GeForce GTX 1080Ti GPU, while the translational error and
rotational error are less than 1.67 cm and 2.5 degree.
