  We propose a novel keypoint voting 6DoF object pose estimation method, which
takes pure unordered point cloud geometry as input without RGB information. The
proposed cascaded keypoint voting method, called RCVPose3D, is based upon a
novel architecture which separates the task of semantic segmentation from that
of keypoint regression, thereby increasing the effectiveness of both and
improving the ultimate performance. The method also introduces a pairwise
constraint in between different keypoints to the loss function when regressing
the quantity for keypoint estimation, which is shown to be effective, as well
as a novel Voter Confident Score which enhances both the learning and inference
stages. Our proposed RCVPose3D achieves state-of-the-art performance on the
Occlusion LINEMOD (74.5%) and YCB-Video (96.9%) datasets, outperforming
existing pure RGB and RGB-D based methods, as well as being competitive with
RGB plus point cloud methods.
