SPN is a CNN that detects a spacecraft bounding box, classifies then regresses attitude, and optimizes position via Gauss-Newton, achieving degree-level attitude and cm-level position errors on real images after training only on synthetic data.
Deepim: Deep iterative matching for 6d pose estimation
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OneViewAll achieves 92.5% ADD-0.1 accuracy on LINEMOD for novel object 6D pose estimation using only one real reference view by integrating category, symmetry, and patch-level semantic priors in a projection-equivariant alignment.
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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Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks
SPN is a CNN that detects a spacecraft bounding box, classifies then regresses attitude, and optimizes position via Gauss-Newton, achieving degree-level attitude and cm-level position errors on real images after training only on synthetic data.
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OneViewAll: Semantic Prior Guided One-View 6D Pose Estimation for Novel Objects
OneViewAll achieves 92.5% ADD-0.1 accuracy on LINEMOD for novel object 6D pose estimation using only one real reference view by integrating category, symmetry, and patch-level semantic priors in a projection-equivariant alignment.
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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.