TransPose: A Transformer-based 6D Object Pose Estimation Network with
  Depth Refinement
Mahmoud Abdulsalam Nabil Aouf
As demand for robotics manipulation application increases, accurate
vision-based 6D pose estimation becomes essential for autonomous operations.
Convolutional Neural Networks (CNNs) based approaches for pose estimation have
been previously introduced. However, the quest for better performance still
persists especially for accurate robotics manipulation. This quest extends to
the Agri-robotics domain. In this paper, we propose TransPose, an improved
Transformer-based 6D pose estimation with a depth refinement module. The
architecture takes in only an RGB image as input with no additional
supplementing modalities such as depth or thermal images. The architecture
encompasses an innovative lighter depth estimation network that estimates depth
from an RGB image using feature pyramid with an up-sampling method. A
transformer-based detection network with additional prediction heads is
proposed to directly regress the object's centre and predict the 6D pose of the
target. A novel depth refinement module is then used alongside the predicted
centers, 6D poses and depth patches to refine the accuracy of the estimated 6D
pose. We extensively compared our results with other state-of-the-art methods
and analysed our results for fruit-picking applications. The results we
achieved show that our proposed technique outperforms the other methods
available in the literature.