  We propose a novel keypoint voting scheme based on intersecting spheres, that
is more accurate than existing schemes and allows for fewer, more disperse
keypoints. The scheme is based upon the distance between points, which as a 1D
quantity can be regressed more accurately than the 2D and 3D vector and offset
quantities regressed in previous work, yielding more accurate keypoint
localization. The scheme forms the basis of the proposed RCVPose method for 6
DoF pose estimation of 3D objects in RGB-D data, which is particularly
effective at handling occlusions. A CNN is trained to estimate the distance
between the 3D point corresponding to the depth mode of each RGB pixel, and a
set of 3 disperse keypoints defined in the object frame. At inference, a sphere
centered at each 3D point is generated, of radius equal to this estimated
distance. The surfaces of these spheres vote to increment a 3D accumulator
space, the peaks of which indicate keypoint locations. The proposed radial
voting scheme is more accurate than previous vector or offset schemes, and is
robust to disperse keypoints. Experiments demonstrate RCVPose to be highly
accurate and competitive, achieving state-of-the-art results on the LINEMOD
99.7% and YCB-Video 97.2% datasets, notably scoring +4.9% higher 71.1% than
previous methods on the challenging Occlusion LINEMOD dataset, and on average
outperforming all other published results from the BOP benchmark for these 3
datasets. Our code is available at http://www.github.com/aaronwool/rcvpose.
