  In this paper, we address the challenging task of estimating 6D object pose
from a single RGB image. Motivated by the deep learning based object detection
methods, we propose a concise and efficient network that integrate 6D object
pose parameter estimation into the object detection framework. Furthermore, for
more robust estimation to occlusion, a non-local self-attention module is
introduced. The experimental results show that the proposed method reaches the
state-of-the-art performance on the YCB-video and the Linemod datasets.
