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.

使用可学习的核函数在高维特征空间中提出了一种新的自监督关键点径向投票型 6DoF PE 框架，以缩小仿真和真实领域之间的差距。通过将网络参数从训练于合成数据和合成位姿的源领域逐步演化到训练于真实数据的目标领域，无需真实的基准数据注释。该方法在三个常用的 6DoF PE 数据集上均获得了最先进的性能，并在所有六个适用的 BOP 核心数据集上与全监督方法相媲美，其结果接近于最好的全监督结果的 - 10.8% 至 - 0.3%。