  Monocular 6D pose estimation is a fundamental task in computer vision.
Existing works often adopt a two-stage pipeline by establishing correspondences
and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose.
Recent works try to integrate differentiable RANSAC algorithms to achieve an
end-to-end 6D pose estimation. However, most of them hardly consider the
geometric features in 3D space, and ignore the topology cues when performing
differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge
Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts
from the following three aspects: 1) We take advantages ofestimated depth
information to guide both the correspondences-extraction process and the
cascaded differentiable RANSAC algorithm with geometric information. 2)We
leverage the uncertainty ofthe estimated depth map to improve accuracy and
robustness ofthe output 6D pose. 3) We propose a differentiable
Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology
relations between 2D-3D correspondences. Experiments demonstrate that our
proposed network outperforms current works on both effectiveness and
efficiency.
