  We address the simulation-to-real domain gap in six degree-of-freedom pose
estimation (6DoF PE), and propose a novel self-supervised keypoint voting-based
6DoF PE framework, effectively narrowing this gap using a learnable kernel in
RKHS. We formulate this domain gap as a distance in high-dimensional feature
space, distinct from previous iterative matching methods. We propose an adapter
network, which is pre-trained on purely synthetic data with synthetic ground
truth poses, and which evolves the network parameters from this source
synthetic domain to the target real domain. Importantly, the real data training
only uses pseudo-poses estimated by pseudo-keypoints, and thereby requires no
real ground truth data annotations. Our proposed method is called RKHSPose, and
achieves state-of-the-art performance among self-supervised methods on three
commonly used 6DoF PE datasets including LINEMOD (+4.2%), Occlusion LINEMOD
(+2%), and YCB-Video (+3%). It also compares favorably to fully supervised
methods on all six applicable BOP core datasets, achieving within -11.3% to
+0.2% of the top fully supervised results.
