A factor graph that fuses motion models with uncertainty-aware pose measurements improves temporal consistency and benchmark scores for vision-based robot control.
Foundpose: Unseen object pose estimation with foundation features
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
MAPRPose reports 76.5% Average Recall on the BOP benchmark for multi-object 6D pose estimation, beating FoundationPose by 3.1% while running 43 times faster through mask-aware proposals and amodal refinement.
citing papers explorer
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Temporally Consistent Object 6D Pose Estimation for Robot Control
A factor graph that fuses motion models with uncertainty-aware pose measurements improves temporal consistency and benchmark scores for vision-based robot control.
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MAPRPose: Mask-Aware Proposal and Amodal Refinement for Multi-Object 6D Pose Estimation
MAPRPose reports 76.5% Average Recall on the BOP benchmark for multi-object 6D pose estimation, beating FoundationPose by 3.1% while running 43 times faster through mask-aware proposals and amodal refinement.