6D object pose estimation based on the attention mechanism

Estimating a 6D object pose is an important and longstanding computer vision task. Though existing deep learning -based approaches have achieved inspiring results, there is still much room for improvement. In this paper, we present to introduce attention mechanism into object pose estimation method, which enables the network extract more distinguishing features on the objects to further get accurate object 6D pose. We verify the proposed method on the LINEMOD dataset, and the results show that our method achieves state-of-the-art performance.