Get object pose using pre-trained synthetic data and no ground truth labels with RKHSPose
Get object pose using pre-trained synthetic data and no ground truth labels with RKHSPose
Pseudo-keypoints RKHS Learning for Self-supervised 6DoF Pose Estimation
arXiv paper abstract https://arxiv.org/abs/2311.09500
arXiv PDF paper https://arxiv.org/pdf/2311.09500.pdf
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.
... formulate this domain gap as a distance in high-dimensional feature space, distinct from previous iterative matching methods.
... 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
... also compares favorably to fully supervised methods on all six applicable BOP core datasets ...
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