Vote from the center: 6 dof pose estimation in rgb-d images by radial keypoint voting

YANGZHENG WU, Mohsen Zand, Ali Etemad, Michael Greenspan

Abstract:

Abstract. 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.