  This paper addresses the simulation-to-real domain gap in 6DoF PE, and
proposes a novel self-supervised keypoint radial 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 evolves the network parameters from the source domain, which has
been massively trained on synthetic data with synthetic poses, to the target
domain, which is trained on real data. Importantly, the real data training only
uses pseudo-poses estimated by pseudo-keypoints, and thereby requires no real
groundtruth data annotations. RKHSPose achieves state-of-the-art performance 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
-10.8% to -0.3% of the top fully supervised results.

Computer Vision and Pattern Recognition