  Real-time 6D object pose estimation is essential for many real-world
applications, such as robotic grasping and augmented reality. To achieve an
accurate object pose estimation from RGB images in real-time, we propose an
effective and lightweight model, namely High-Resolution 6D Pose Estimation
Network (HRPose). We adopt the efficient and small HRNetV2-W18 as a feature
extractor to reduce computational burdens while generating accurate 6D poses.
With only 33\% of the model size and lower computational costs, our HRPose
achieves comparable performance compared with state-of-the-art models.
Moreover, by transferring knowledge from a large model to our proposed HRPose
through output and feature-similarity distillations, the performance of our
HRPose is improved in effectiveness and efficiency. Numerical experiments on
the widely-used benchmark LINEMOD demonstrate the superiority of our proposed
HRPose against state-of-the-art methods.
